and the fields of

• The Design of Autonomous Agents - A Layered Approach 1996 and Distributed Intelligent Agents 1996, which are about the Reusable Task Structure-based Intelligent Network Agents (RETSINA),
• distributed Multi-Agent Reasoning System (dMARS) 1997, which is an extension of the Procedural Reasoning System (PRS) and based on the Belief-Desire-Intention (BDI) agent architecture,
• Mole - A Java based Mobile Agent System 1997,
• Agent Factory 1999,

  FIPA compliant

• ZEUS: An advanced Tool-Kit for Engineering Distributed Multi-Agent Systems 1998,
• Java Agent DEvelopment Framework (JADE) 1999, can be specialized to BDI as done with extension JADEX 2003, and
• JACK Intelligent Agents 1998, which is based on dMARS, but BDI since version 1.2 October 1998,

and also

we have reviewed the detail about "middleware for the development and run-time execution of peer-to-peer applications which are based on the agents paradigm".
"These same [middleware] services are provided by operative systems[, better known as operating systems], but the idea behind middleware is to provide better, OS independent APIs aggregating native facilities into simple-to-reuse building blocks."
But eventually, one has the API of an os and the API of a middleware and also the overhead by the additional middleware layer of the overall system stack, though we also showed with our Evolutionary operating system (Evoos) that it never made sense for such an overall system design.

As we explained in the past, we made several revolutionary steps with our creations:
In a first step, we reduced the height of the system stack by removing the

respectively by providing their functionality (in case of a monolithic os) in the kernel (layer) or (in case of a microkernel-based os or a kernel-less os) in an integrated system architecture or (in case of a hybrid os) in both to reduce complexity and unleash performance gains by retaining the advantages of Object-Oriented Programming (OOP 1), Agent-Oriented Programming (AOP), component-based system development, middleware, and so on.
Also important to note are the many improvements, that made the overall OS possible at all, such as

Simply said, we just did best practice in all related fields and created new solutions.

In a second step, we did the same with Agent-Oriented Programming (AOP), Intentional Programming (IP) for intentional agents for intentional humans, and multimodality, and also resilience.

In a third step, we did the same with informatics, physics, chemistry, biology, etc., cybernetics, bionics, ontonics, realities, etc., Rechnender Raum==Computing Space and Caliber/Calibre.

In a further step, we also generalized the

in various ways, such as

with the creation of our Evoos and our OS.

In general, a clear cut is complex, but nevertheless many points exist where the fine line is drawn.

The boundary runs between

(see also for example the book From P2P to Web Services and Grids: Peers in a Client/Server World. 2005),

and also

and also

In general,

• operating system (os) (e.g. Real-Time os (RTos), Distributed os (Dos)),
• Virtual Machine (VM),
• SoftBionic Computing (SBC) (e.g. Software Agent and Soft Computing respectively Soft Agent Computing (SAC)),
• SoftWare Agent (SWA) or softbot respectively Agent-Based System (ABS) or Agent System (AS), and Agent-Oriented technologies (AOx), including Agent-Oriented Programming (AOP),
• Distributed Computing (DC) or Distributed System (DS) (e.g. Peer-to-Peer (P2P) Computing (P2PC)),
• Real-Time Computing (RTC) or Real-Time System (RTS),
• Intelligent Networking (IN),
• Ubiquitous Computing (UbiC) and Internet of Things (IoT),
• VR,
• etc.,

• os,
• P2P VM,
• Scalable Distributed Computing (SDC),
• resilience (e.g. fault tolerance, trustworthiness (e.g. reliability, availability, safety, security, performability (Quality of Service (QoS)))),
• model-based paradigm,
• ontology-based and Ontology-Oriented (OO 2) paradigms,
• SBC (e.g. BDI SAC, Real-Time Artificial Intelligence (RTAI)),
• Smart Work Manager (SWM),
• Affective Computing (AffC),
• Service-Oriented technologies (SOx),
• Space-Based technologies (SBx),
• Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Virtualized Network Function (VNF),
• Cloud, Grid, Edge, and Fog Computing (GCWFC) respectively Space and Time Computing and Networking (STCN),
• 5G Next Generation (5G NG),
• Cyber-Physical System (CPS) (e.g. Intelligent Environment (IE), Industrial Internet of Things (IIoT)),
• 21st century technologies,
• etc.,

and

• os,
• RTC,
• SDC,
• SBC P2P VM,
• Autonomic Computing (AC),
• Cognitive Computing (CogC),
• Resource-Oriented Computing (ROC),
• microService-Oriented Architecture (mSOA),
• os-level Virtualized Network Function (VNF) (e.g. Cloud-Native Network Function (CNF) respectively Space and Time Network Function (STNF)),
• Space-Based Agent-Based System (SBABS),
• Real-Time Agent-Based System (RTABS) (e.g. BDI, hybrid, holonic, MAS, CAS),
• Intelligent Assistant (IA) or Intelligent Personal Assistant (IPA),
• crypto (smart contract transaction, blockchain, Distributed Ledger Technology (DLT)),
• AR, AV, MR, XR, SemR, SynR, NR,
• Caliber/Calibre,
• Ontologic System (OS),
• Ontologic(-Oriented) (OO 3) paradigm respectively Ontologic Computing (OC),
• overall Ontologic System Architecture (OSA) and infrastructure,
• convergence of Internet, World Wide Web (WWW), Semantic (World Wide) Web (SWWW), ubiquitous, immobot infrastructure or Internet of Things (IoT), Semantic Web of Things (SWoT), Mixed Reality (MR), etc.,
• 21st century technologies,
• etc..

Our improvements, creations, designs, alignments, integrations, and overall approach and architecture have become the industry standard(s). All entities want to do what C.S. and our corporation do. The public and the market decided for us in the course of a legal and fair competition.

In general, All or nothing at all. Yesterday and not now and not in the future. In fact, large scale entities, such as federal authorities, research institutes, industrial companies need all anyway.

For the main fields (of interest) see the issue SOPR #327 of the 7th of June 2021 about the Main Contract Model (MCM) in relation to the exclusive infrastructures of our Society for Ontological Performance and Reproduction (SOPR).

The mandatory things are a reaction to the activities, which are happening since the year 1998, and to keep the royalties low.
A share of 15 to 20% of revenue for the performance and reproduction of certain parts of our Ontologic System (OS), including our Evolutionary operating system (Evoos), With All Discounts Granted (WADG) for Information and Communication Technology (ICT) licensee class (maybe Alphabet→Google suggested 15% once again) for at least 10 years (2030 with all damage compensations, admission fees, and royalties as discussed) are Fair, Reasonable, And Non-Discriminatory, As well as Customary (FRANDAC) terms and conditions.

Explain it (once again) to the politicians, legal teams, and shareholders.

We have a lot to do for at least the next 20 or 50 years. Have fun.
Welcome to the Ontoverse (Ov).
Welcome to the New World Order (NWO).

07.April.2022

Ontonics Further steps

We made considerable progress with a basic element, which is a part of another single solution, which again is also some kind of a grid, structure, or network and a part of a larger overall integrated solution, but pursues different goals and accordingly has different related properties and functionalities and also smaller dimensions than other basic elements and single solutions.

10.April.2022

Ontonics Further steps

We heard that the Fédération Internationale de l'Automobile (FIA) is revising at least its regulation for the Formula One World Championship, which will be valid from the year 2026.

But most potentially, our Race Changer™ team of our

will not be allowed to compete in the Formula One World Championship with our Purely Electric&153; powertrain and other Game Changer™ and Race Changer™ technologies (see also the Further steps of the 8th of December 2021), because we just forgot that the utilization of patented technology is not allowed by its already regulations already in force. ): :D

13.April.2022

00:33 UTC+1
Clarification

*** Work in progress - some quotes and comments missing ***
We continued the work on our website revision and clarification in relation to the fields of

Obviously, there was another fraudulent group acting behind the curtain and damaging a lot of the work of C.S. titled "Evolutionary operating system (Evoos)" with their espionage or other illegal and even serious criminal activities. We even got some more names.

Our further research of works of research and development done around the years 1990 to 2002 showed another fraudulent group, specifically the gangs around theT(ele)Communications Service Providers ((T)CSPs) and Information and Communications Technology (ICT) manufacturers

and multimedia and middleware, etc., and also

inflicted a lot of damage to the work of C.S. after they found out what we creating, researching and developing, and implementing with our Evoos like other entities related to Deutsche Telekom, Volkswagen, and SAP, as we well as Microsoft, Motorola, and Hewlett-Packard.

But they all together were not able to steal all in some few months, because our Evoos was just

and therefore has not been understood by them at first.

In this Clarification we will show what and what all these deficits mean for the future things at that time respectively actual things at this time.

We gathered more interesting informations (again), which partly disprove us or correct some statements of us, which again were not quite right, but also show deficits of the others, which we have all solved, and in this way allow to find the legal ground and to draw a better clear cut, if required to convince designated members of our SOPR.
One example is the integration of the fields of

which both were already trends around the end of 1996, to the fields

But there are always significant details to note and regard,
to note and consider,
to keep in mind and consider,
to consider and take into account,
when it comes to legal issues.

largely complementary to Evoos or missing a lot
many deficitis in the details
often
no deliberative (e.g. BDI), therefore
no deliberative and reactive respectively no hybrid, therefore
no immobot

often
asynchronous agent model, or Actor- and Agent-Oriented Programming (AAOP) paradigm, because distributed synchonization very time-consuming

Interestingly, we have observed and worked out many attempts to rescue and cure deficits of prior art all the time in the year 1999 and the following years, often by applying cheap tricks to mislead the public.
Around the years 1998 and 1999, we already found out that developers of frameworks and middleware in the field of Multi-Agent System (MAS) have no clue about the fields of operating system (os) and Distributed System (DS), including Peer-to-Peer (P2P) Computing System (P2PCS), as well as resilience, and so on, and began to reinvent already existing solutions of the latter fields.

We continue the discussion of the various fields and related works in relation to

Correspondingly, the quoted and commented works include:

We quote some documents related to the fields of

We quote a document, which is related to organization, messaging, negotiation, and blackboard system, and was publicated in 1996: "Co-ordination in software agent systems
[...] co-ordination in multi-agent systems [...]

[...]

Organisational structuring
This is the simplest co-ordination scenario which exploits the a priori organisational structure. [...] Hierarchical organisations abound, yielding the classic master/slave or client/server co-ordination technique, used typically for task and resource allocation among slave agents by some master agent. This technique is implemented in a couple of ways.

The latter point highlights the fact that organisational work ought not to be solely associated with hierarchies. For example, in the DVMT system [12 [The Distributed Vehicle Monitoring Testbed: A Tool for Investigating Distributed Problem-Solving Networks. 1983]] which also exploits a blackboard, co-ordination occurs among peer agents.
Other organisational structures exist, of course, including the centralised and decentralised market structures. The centralised market structure employs a master/slave co-ordination approach while a contracting technique [...] is more suitable for a decentralised market structure.

Critique
These strategies are useful where there are master/slave relationships in the MAS being modelled. Much control is exerted over the slaves' actions, and hence the problem solving process. However, such control, in its extremes, mitigate against all the benefits of [Distributed Artificial Intelligence (]DAI[), Multi-Agent and Cooperative Computing (MACC), or Modeling Autonomous Agents in a Multi-Agent World (MAAMAW)] - speed (due to parallelism)[,] reliability, concurrency, robustness, graceful degradation, minimal bottlenecks, etc. In the blackboard coordination scheme, with no direct agent-to-agent communication, a severe bottle-neck may result if there are many agents, even in the case of multi-partitioned blackboards. Furthermore, all agents would need to have a common domain understanding (i.e. semantics). For this latter reason, most blackboard systems tend to have homogeneous and rather small-grained agents as is the case in the DVMT prototype [12]. Durfee et al [13 [Trends in Cooperative Distributed Problem Solving. 1989]] point out that such centralised control as in the master/slave technique is contrary to the basic assumptions of DAI. It presumes that at least one agent has a global view of the entire agency - in many domains, this is an unrealistic assumption.

[...]
Werkman proposes a knowledge-based model of an incremental form of negotiation [10]. Werkman's Designer Fabricator Interpreter (DFI) model, it is claimed, is based largely on various human models of negotiation. This scheme uses a shared-knowledge representation, called shareable agent perspectives, which:
"...allows agents to perform negotiation in a manner similar to co-operating (or competing) experts who share a common background of domain knowledge."
Essentially, it exploits a blackboard with partitions for requested proposals, rejected proposals, accepted proposals, a communications partition and shared knowledge. Such rich detail and knowledge of the perspectives of other agents provide invaluable information for agents to make better proposals in the future.
[...]
[...] The use of arbitration is relatively novel. His agents communicate via the blackboard through a speech-act [speech act-]based language. The centralised blackboard could be a bottle-neck, and, without an explicit scheduler, reading and posting to the blackboard seems chaotic."

Comment
The quoted document is basically about negotiation (16 references of 40 include this term) and cooperation.

Peer agents only means coequal agents, but not Peer-to-Peer (P2P) Computing, and it is also about "centralised blackboard". Therefore, no combination and integration of Peer-to-Peer (P2P) Multi-Agent System (MAS) (P2P MAS) (e.g. Java Agent) and blackboard, tuple space system (e.g. Java Jini), and even not Scalable Distributed Tuplespace (SDT) (see for example the related notes publicated in March 2019) and Scalable Content-Addressable Network (SCAN), Space-Based Agent System (SBAS), Space-Based technologies (SBx), and Service-Oriented technologies (SOx).
Java Jini was new and not understood, but solves the problems (see also the document titled "Using JavaSpaces to create adaptive distributed systems" 2002 and discussed in the OntoLix and OntoLinux Website update of the 10th of March 2019 and the Clarification of the 18th of January 2020 and 14th of August 2020 (keywords Scalable Infrastructure (SI) and ServiceFrame)).
But our Evoos already does it through Associative Memory (AM) of a brain, and also a holonic brain and multiple brains as part of a Distributed System (DS) and our OS makes it explicit through the inclusion of Evoos, the CHemical Abstract Machine (CHAM) in OntoBot, and also the other Content-Addressable technologies.

Also note the little historical detail that the MASs based on blackboard were developed before the development of the BDI architecture (1995), the immobot (1996), and the support for and use of ontologies, and ActorAgent also came with our Evoos or even before with concurrent agent systems.
Also Multimodal User Interface (MUI) and Dialogue Management System (DMS) based on PVM and distributed multi-blackboard not based on Agent-Based System (ABS), specifically MAS and P2P MAS middleware.

Also note that the integrations of

imply serverless.

We quote a series of slides, which is about the BDI agent architecture and was publicated on the 29th of November 2002: "Progress in BDI Logic Programming with AgentSpeak(L)

[...]

AgentSpeak(XL) Interpreter
Introduction

• using decision-theoretic task scheduling (TAEMS/DTC) to improve intention selection in AgentSpeak(L)

• applications where quantitative reasoning is natural
• control over an agent's set of intentions

Language Extensions (I)

Language Extensions (II)

• Run locally by the interpreter, do not affect the environment (i.e., they take effect immediately)
• Can be used in the context as well as in the body of a plan
• [...]

[...]

Integrating with DTC

[...]

AgentSpeak(F) Verification
Introduction

[...]

Specifications (I)

[...]

Ongoing and Future Work
[Multi-Agent Simulations for the SOCial sciences (]MASSOC[)]

• Social aspects of urban development [...]

Abstract Syntax and Structural Operational Semantics of AgentSpeak(L)"

Comment
In general, validation or verification does not imply that it is done by model checking.
In fact, model checking on the basis of Linear Time Temporal Logic or Linear Temporal Logic (LTL) and later with the model checker Spin was introduced around the year 2002 and is also discussed in the comment to the quoted document titled "Progress in BDI Logic Programming with AgentSpeak(L)" (see also the related document titled "Model Checking AgentSpeak", based on AgentSpeak(F), and publicated in July of 2003) and the comment to the quoted document titled "Agent Programming in 3APL" or "A Formal Embedding of AgentSpeak(L) in 3APL".

We quote a document, which is about the fields of Cognitive Computing (CogC), Intelligent Agent System (IAS) and Cognitive Agent System (CAS), and Agent-Oriented Programming (AOP), and An Abstract Agent Programming Language (AAAPL or 3APL), and was publicated on the 1st of November 1999: "Agent Programming in 3APL

Abstract
An intriguing and relatively new metaphor in the programming community is that of an intelligent agent. The idea is to view programs as intelligent agents acting on our behalf. By using the metaphor of intelligent agents the programmer views programs as entities which have a mental state consisting of beliefs and goals. The computational behaviour of an agent is explained in terms of the decisions the agent makes on the basis of its mental state. It is assumed that this way of looking at programs may enhance the design and development of complex computational systems.
[...] 3APL has a clear and formally defined semantics. The operational semantics of the language is defined by means of transition systems. 3APL is a combination of imperative [(Java)] and [not purely declarative] logic programming [(Prolog)]. [...] States of agents, however, are belief or knowledge bases, which are different from the usual variable assignments of imperative programming. From logic programming, the language inherits the proof as computation model as a basic means of computation for querying the belief base of an agent. [...] Moreover, on top of that 3APL agents use so-called practical reasoning rules which extend the familiar recursive rules of imperative programming in several ways. Practical reasoning rules can be used to monitor and revise the goals of an agent, and provide an agent with reflective capabilities.
[...] a program is taken as an entity with a mental state, which acts pro-actively and reactively, and has reflective capabilities. [...] Furthermore, we provide a language with a formal semantics for programming control structures. The main idea is not to integrate this language into the agent language itself, but to provide the facilities for programming control structures at a meta level. The operational semantics is accordingly specified at the meta level, by means of a meta transition system.

Introduction
[...] The computational behaviour of an agent is explained in terms of the decisions it makes on the basis of its belief and goals. [...]
[...] agent oriented programming [...] the proof as computation model of logic programming is used to implement the querying of the beliefs [of] an agent. Whereas concepts from imperative programming are used to describe the execution of the goals of an agent. On top of that, the programming language supports the programming of agents which have reflective capabilities related to their goals or plans. These reflective capabilities are provided by so-called practical reasoning rules.
[...] We believe it is particularly important to specify the formal semantics of an agent language. [...] The semantics of 3APL we present in this paper is an operational semantics. The operational semantics is defined in terms of transition systems ([18 [A structural approach to operational semantics. 1981]]). [...]
In the second part of the paper, we extend our framework to deal with the problem of programming control structures for agents. The issues that arise at this level concern the selection of goals or plans and the selection of practical reasoning rules. We distinguish between an object level which concerns the programming of agents in the agent language 3APL and a meta level which concerns the programming of control structures for agents in a meta level programming language. This meta language is defined by means of transition systems again, and we illustrate its use by implementing a new control structure in this language. [...]

[...]
[...] it is important to provide a logic of agents to reason about the agents written in the agent programming language. [...]
[...] We think that most researchers would agree that (at least most of) the following characteristics are constitutive of intelligent agents:

Intelligent agents are goal-directed entities, which means that they have a set of goals and associated plans which result in the execution of primitive or basic actions. To realise their goals, agents need to find suitable plans which involves a particular type of reasoning known as practical reasoning. Moreover, an agent needs to have the ability to monitor its success or failure, which requires reflective capabilities with respect to its goals or plans. Finally, beliefs represent the environment from the agent's point of view, and to keep its beliefs up to date an agent should be able to revise its beliefs.
In short, we think an agent programming language should support these concepts and associated mechanisms of updating. Therefore, an agent programming language should include features for:

[...]

Beliefs
The beliefs of an agent are formulas from some logical language L. This language is used for knowledge represention purposes. In principle, the choice of the knowledge representation language is not fixed by the programming language [...].

Goals and actions
In common sense, and in most logics of agents, the goals of an agent describe the state of affairs an agent would like to be realised. These goals are declarative in nature, and are also called goals-to-be. Although much research has gone into clarifying the notion of a declarative goal, it is still not very clear how to integrate such goals, apart from the most simple cases, into either agent programming languages or agent architectures. [See the documented titled "A Programming Language for Cognitive Agents [] Goal Directed 3APL" and publicated in 2003.]
There is, however, a second, more procedural notion of goal. These goals are also called goals-to-do, because they specify a plan of action the agent is intending to execute. Also, the motivational concept of intention has been analysed in terms of plans ([1 [Intentions, Plans, and Practical Reasoning. 1987]]). In Artificial Intelligence, moreover, the concept of a plan has since long been recognised as similar to that of an imperative program.
[...]
[...] we introduce basic actions, which constitute one of the simple goals in the language. Although it may well be that an agent changes its environment through some interface which depends on the execution of basic actions in the agent language, there is nothing in the agent language which requires such an interface with an environment external to the agent. The use of intelligent agents to control robots is an example where such an interface is required. Personal assistants, which are agents to maintain an agenda of their user, however, do not require any interface to control some external environment. However, in both cases the beliefs of an agent represent something external to that agent itself. Blocks and other objects in the first case, activities, locations and so on, in the second case.
[...] basic actions are actions which affect the mental state of the agent (although these actions, as we noted in the previous paragraph, in practice may be connected with some interface). Basic actions are actions which update or change the beliefs of an agent. This is most natural, since these updates change the representation of the environment the agent is supposed to control (or represent, in the case of personal assistants) by means of performing actions. [...]
[...]

Remark
We use the notion of a goal rather than that of intention. The reason is that the notion of a goal is a more general notion than that of intention. Intentions are usually viewed as some kind of choice with an associated level of ommitment made to that choice ([2]). [...] In the programming language, a goal does reflect a choice the agent has made. However, there is no explicit level of commitment associated with each of the goals of an agent. The commitment strategies or revision strategies of an agent are more or less implicit in the practical reasoning rules of an agent. The practical reasoning rules of an agent thus determine whether or not a goal can be considered as an intention.

Practical Reasoning Rules
[...] The main purpose of practical reasoning rules is that they supply the agent with a facility to manipulate its goals. Whereas goals operate on the beliefs of an agent, rules operate on the goals. These rules both facilitate planning and monitoring of the goals of an agent. Practical reasoning rules can be used to build a plan library from which an agent can retrieve plans for achieving a[n] achievement goal p(t ). They also can be used to provide the means to revise and monitor goals of the agent.
The name of these rules derives from the role they play in the operation of intelligent agents. They supply agents with reasoning capabilities to reflect on their goals. The type of reasoning involved is similar to a certain extend to the practical reasoning used by human agents to achieve their goals. Informally, this type of common sense reasoning [...] means-end reasoning [...] Note that in our explanation of practical reasoning the conclusion of this type of reasoning is the adoption of a new goal (and not the performance of some action, as some philosophers claim should be the result of practical reasoning).
[...] The reasoning involved in reconsidering one's goals follows a similar pattern as the means-end reasoning [...].
[...]

Classification of rules
[...]

[...]

Intelligent Agents
[...]
The dynamic components of an agent are its beliefs and goals. During the operation of an agent these are the only components which can change. The set of practical reasoning rules associated with an agent does not change during the operation of an agent [(Footnote:] Although this may seem restrictive, dynamically hanging the practical reasoning cabilities of an agent in a sensible way seems to require facilities which are not easily provided by introducing new constructs into the agent programming language; however, we will make a suggestion related to this issue concerning the introduction of new practical reasoning rules by means of a plan action in the meta language for programming control structures which is introduced below.[)]
[...]
Summarising, 3APL is a combination of imperative and logic programming. Whereas imperative programming constructs are used to program the usual flow of control from imperative programming 3 and update the current beliefs of the agent by executing basic actions, logic programming implements the querying of the belief base of the agent and the parameter mechanism of the language based on computing bindings for variables.
In the agent programming language 3APL three conceptual levels can be distinguished: beliefs, goals, and practical reasoning rules. At the most basic level, the beliefs of an agent represent the current situation from the agent's point of view. At the second level, the execution of goals operate on the belief base of an agent by adding and deleting information. At the third level, practical reasoning rules supply the agent with reflective capabilities to modify its goals. This cleanly separates the different types of updating. From a more traditional perspective, the beliefs of the agent correspond to the state of the system and the goal base of an agent represents the program which is being executed. Because of the refletive capabilities of agents to modify their goals in arbitrary ways by means of rules, however, agents are not just programs in the traditional sense, but are self-modifying programs. This is a distinguishing feature of intelligent agents in the agent language 3APL.

Operational Semantics
The dynamics of an agent corresponds to changes in the mental state of that agent. In this section, we provide a formal semantics which formalises these dynamical aspects of an agent. The semantics specifies how the operation of an agent affects the mental state of that agent. The semantics we use in this paper is an operational semantics defined by means of transition systems. Operational semantics provides a constructive approach to semantics, in contrast with a denotational semantics which provides a more abstract, mathematical type of semantics.

Transition Systems
[...]
A transition relation is a relation on so-called configurations. A 3APL configuration consists of three components. The first two components correspond to the current mental state of an agent. The first component consists of the the goal base of the agent and the second component consists of the belief base of the agent.
[...]

Selection Mechanisms and Control Structures
[...]
Although a black-box approach is suitable for most languages, we argue that for agent languages a glass-box approach is more appropriate. We think that in the case of agent programming the problem is better seen as a semantic problem. [...]
[...]

A Language for Programming Control Structures
[...] we could separate the basic features of agents and the selection mechanisms and specify them in two different semantic systems. This approach supposes that it is possible in the semantic system which defines the control structure to refer to the semantic objects in the semantics of the agent language and introduce a distinction between an object and meta level semantics. We have chosen for the second option (cf. also 13 [Control Structures of Rule-Based Agent Languages. 1999]). The main reason for this choice is that the specification of the selection mechanisms at a meta level gives us two independent systems. This gives us the usual advantages associated with the modularity of two different systems. For instance, by separating the two semantic systems any suitable agent language specified at the object level can be plugged in in any control structure specified at the meta level. [...]
[...]
The main idea, thus, is to separate the semantic specification of the agent language and its control structure. This approach calls for a distinction between an object and a meta level. At the object level, the transition system which was introduced earlier defines the semantics of the agent language.At the meta level, a second transition system is introduced to define the semantics of the meta language for programming control structures. [...]
[...]
Since the main issue that a control structure for agent languages has to deal with is the selection of goals and rules from sets of goals and rules, a set-based language seems to provide the right abstraction level to discuss the control structure for an agent language. Summarising, the meta language is an imperative, set-based language. It has terms for referring to the goals and rules of an agent. Furthermore, the meta-language includes assignment, four task-specific basic actions, and the usual operators from imperative programming.

[...]

A Control Structure for 3APL
[...] The control structure that we propose for 3APL is a specialisation of the well-known update-act cycle. The update-act cycle is used in a number of agent languages (cf. [14 [Towards a unified agent architecture that combines rationality with reactivity. 1996]], [20 [AgentSpeak(L): BDI Agents Speak Out in a Logical Computable Language. 1996], [22 [Agent-oriented programming. 1993]]). [...]

Comparison With Other Languages
[...] AGENT-0 ([22]) and AgentSpeak(L) ([20]), and [...] ConGolog ([7 [Reasoning about concurrent execution, prioritized interrupts, and exogenous actions in the situation calculus. 1997]],[][16 [Foundations of a Logical Approach to Agent Programming. 1996]]) [...]

AGENT-0
[...] The commitments of AGENT-0 agents correspond to the goals of 3APL agents. AGENT-0 lacks constructs like those of imperative programming to program control flow, but uses explicit representation of time and a global clock to impose some order on the execution of commitments. Commitments are basic actions with a time stamp. The commitment rules correspond to the practical reasoning rules of 3APL. However, commitment rules cannot be used to revise commitments, but can only be used to add new commitments.
[...]
[...] 3APL has been extended to a multi-agent language with communication primitives (cf. [9 [Semantics of Communicating Agents Based on Deduction and Abduction. 1999]]), and a formal semantics for these primitives has been provided.

AgentSpeak(L)
[...] In another paper [10 [A Formal Embedding of AgentSpeak(L) in 3APL. 1998]], we showed that the notions of event and intention can be reduced to the notion of goal in 3APL. Again, the range of rules in 3APL is much larger than that of AgentSpeak(L) which only provides for plan rules. This means that AgentSpeak(L) lacks the means to modify or revise goals.
[...]
[...] AgentSpeak(L) can be viewed as an imperative programming language with belief bases as its domain of computation. Because of the obvious correspondence of a belief base and a database, it is an interesting question what connections there are between agent languages like AgentSpeak(L) and database programming.

ConGolog
[...] we are more concerned with the dynamics of the agent's mental life. In our view we provide a more dynamic perspective contrasting the static planning of ConGolog. However, the proposal of a so-called incremental interpreter for ConGolog also provides a more dynamic perspective ([3 [An incremental interpreter for high-level programs with sensing. 1998]]).

Conclusion
[...]
[...] The particular control structure for 3APL is a special case of the update-act cycle. A control structure is a solution to the problem of which goals to select for execution and which rules to select for application. [...] Since the semantics of the meta language is specified by means of a second transition system, the total agent system is a two level system. The so-called object level corresponds to the agent language, while the meta level corresponds to the meta language for programming control structures.
[...]
[...] some of the issues that remain for future research [...] extension of the agent language with communication and possibly with other multi-agent features [...] denotational semantics, and corresponding proof theories, in order to obtain a method for the design of agents [...] obtain a better understanding of practical reasoning rules and their use [...] extend the ordering to a dynamic ordering [...] assigning priorities to goals [...]"]

For better understanding, we also quote a document, which is about an BDI extension of a FIPA compliant agent platform and was publicated on the 1st December of 2005: "JADEX Userguide Release 0.941
[...]
To realise rational agents, numerous deliberative agent architectures exist (e.g. BDI [Bratman 1987], AOP [Shoham 1993], 3APL [Hindriks et al. 1999 [(the document about 3APL quoted above and commented below)]] and SOAR [Lehman et al. 1996] to mention only the most prominnent ones). In these architectures, the internal structure of an agent and therefore its capability of choosing a course of action is based on mental attitudes. The advantage of using mental attitudes in the design and realisatiio realisation of agents and multi-agent systems is the natural (human-like) modelling and the high abstraction level, which simplifies the understanding of systems [McCarthy et al. 1979]. Regarding the theoretical foundation and the number of implemented and successfully applied systems, the most interesting and widespread agent architecture is the Belief-Desire-Intention (BDI) agent architecture, introduced by Bratman as a philosophical model for describing rational agents ([Bratman 1987])."

Comment
As with many of the other quoted cases we have an odd timing around the years 1998 and 1999 and an odd overall timeline.

Specifically, 3APL was only about formal specifications, formal semantics, and formalizing BDI agent architectures in July 1998.
In the document titled "A Formal Embedding of AgentSpeak(L) in 3APL" and publicated on the 13th of July 1998 the similarities and differences between BDI AgentSpeak(L) and 3APL were shown.
But around 16 months later in November 1999 3APL was about an operational semantics and a transition system, which reminds us of the

The later is also no surprise anymore.

Also note the confusion about being reactive and changing an internal belief about an external environment or mental, internal environment and an external environment.
Intelligent Agents: Theory and Practice, 1994: "For our purposes, we shall define a reactive architecture to be one that does not include any kind of central symbolic world model, and does not use complex symbolic reasoning. [...] In a 1985 paper, he outlined an alternative architecture for building agents, the so called subsumption architecture (Brooks, [A robust layered control system for a mobile robot,] 1986)."

update-act cycle in agent languages (e.g. hybrid agent architecture and "Towards a unified agent architecture that combines rationality with reactivity". 1996)

only robot and rudimentary Intelligent Personal Assistant (IPA), but no Autonomic Computing (AC), and Resource-Oriented Computing (ROC)

Indeed, 3APL was only a deliberative agent architecture, like BDI, AOP, and SOAR, and a model-based software agent (softbot) at first in 1999, which has the capability to revise and modify goals and beliefs of an agent. But it is reactive respectively active in time only on the deliberatively layer and there existed no reactive layer in 3APL at that time at all and therefore 3APL {correct formulation} was not reactive respectively active or responsive in time and hence was not a hybrid agent architecture and hence was not (suitable for) a Model-Based Autonomous System (MBAS) or Immobile Robotic System (ImRS or Immobot).

no real-time hybrid agent archictecture and {?} BDI from mobile robots for Immobot {came only with cognitive robots in 2003}.

The reactive layer of a hybrid agent architecture and the cognitive robot came only in February 2003.
The usage of "3APL as Programming Language for Cognitive Robots" was discussed in a master's thesis presented only on the 25th of February 2003, which also shows the initial lack of the properties of a reactive agent architecture and a hybrid agent architecture. In fact, there was only {the discussion about} an interface with an environment external to the agent based on the notion of basic actions in 1999.
"[...] We want to use this programming language to implement the control of a mobile robot [(Pioneer 2)]. [...]
[...]
An external program that receives the basic actions from the agent was needed. This program will execute the action in a certain environment. The external program is called "Robot Operating System" (ROS). The environment that was used for this research, is a static fixed environment. It consists of four walls at known positions within a coordinate system in a rectangular space. The agent controls a mobile robot in this environment. The ROS runs on the robot's build-in computer and translates the basic commands to direct movement control.
[...]
The ROS consists of a scheduler, world model, self-localizer and a robot interface. [...]"

Keep in mind that the document about the JADEX quoted above was publicated in 2005 and that only SOAR had this natural (human-like) motivation and point of view, but is very different in comparison to BDI, AOP, and 3APL.
Furthermore, we are not sure and never had the impression that Bratman really wanted to discuss a philosophical model, when presenting the BDI architecture for rational agents.
Also note that mental attitudes or mentalistic features via use of ACL and their use for formalizing and standardizing the semantics of an ACL has considerable limitations (see the documents about the FIPA-OS quoted below and for example Singh, M.P. "Agent communication languages: Rethinking the principles" 1998, and Charlton, P., Cattoni, R., Potrich, A., and Mamdani, E. "Evaluating the FIPA standards and its role in achieving cooperation in multi-agent systems" 2000).
Also note that

One of the "characteristics are constitutive of intelligent agents: [...] agents have reflective or meta-level reasoning capabilities", but no reference is given and following documents reference this quoted document.
"Because of the reflective capabilities of agents to modify their goals in arbitrary ways by means of rules, however, agents are not just programs in the traditional sense, but are self-modifying programs. This is a distinguishing feature of intelligent agents in the agent language 3APL."
In the document titiled "Intelligent Agents" and publicated October 1994 (see the Clarification of the 18th of February 2022) these characteristics are discussed in relation to notions of agency, and updating of beliefs of the Procedural Reasoning System (PRS), but terms like reflective or multi-level reasoning capabilities are not used.
In the document titled "Integrating Reactivity, Goals, and Emotion in a Broad Agent" and publicated in 1992 in relation to the Oz project (see the document titled "Virtual reality, art, and entertainment" and publicated in 1992) (see once again the Clarification of the 18th of February 2022) the agents are also capable to modify their goals.
Also very conspicuous is the emphasized concern regarding the "dynamics of the agent's mental life" in 3APL and the note about an "incremental interpreter".
Even more surprisingly (not really) is the fact that all of this reflects, or better said equals the chapter Neue Anforderungen an Betriebssysteme aus der Sicht der Software-Technologie==New Requirements for Operating Systems from the Perspective of Software Technology of The Proposal, specifically its chapters

The extension with communication is straigthforward and also mentioned in the chapter Conclusion, but "Extending 3APL with Communication" was presented only on the 17th of June 2002 "Conclusion - Transition rules not very suitable for defining semantics - FIPA ACL semantics are not complete" and discussed in a masters' thesis presented only on the 1st of September 2002.

The master's thesis titled "3APL Platform" and only dated 2nd of October 2003, which references the master's thesis titled "Extending 3APL with Communication" and only dated 1st of September 2002 as "Multi-agent FIPA compliant 3APL agents", though we only found a short webpage, which merely contains the abstract of this thesis and some few additional informations, such as "We have chosen the FIPA ACL as our communication language".

This quoted document was also publicated in Autonomous Agent and Multi-Agent System 2 in 1999, but MAS and communications on the basis of FIPA only came in 2002.

FIPA uses frame-based ontologies and an ontology service for the lifecycle management, the service interfaces, and the speech-act [speech act] communications of agents, and the domains respectively semantics of the message contents for interoperabiltiy.
See also FIPA frame-based core ontology for life-cycle management and domain ontologies for communication services of agents, and domain and content semantics.
FIPA only uses ontologies for the life-cycle management and communication services for agents and the domains respectively contents of the messages.
"FIPA platforms defines a standard base protocol based on speech acts[,] several standard ontologies: a core one for registering and querying de-registering services [part of the FIPA management ontology] and various domain specific ones. No specific service encoding is mandatory. Non-FIPA platforms require the use of platform specific service ontology, encoding, and protocol combinations."
A later version of 3APL is extended and designed to support the FIPA standard, {but thesis says that FIPA-ACL was chosen}, but uses only KQML for messaging and agent communication.

In the document titled "A Protocol-Based Semantics for FIPA'97 ACL and its Implementation in JADE" and publicated in 1999 "[...] there are considerable limitations of intentionality as a basis for defining the semantics of an ACL (see also [Singh [Agent communication languages: Rethinking the principles. December] 1998]).", which was also publicated as "A Protocol-Based Semantics for an Agent Communication Language" some few weeks later in 1999 "[...] there are considerable limitations of using mental attitudes for standardising the semantics of an ACL [cf. Singh, 1998]."
Both documents are in very large parts the same with one author of CSELT (Italy Telecom Lab) of the first document and one author of Imperial College of the second document, and one fraudulent author in both documents, which shows the very suspicious and odd connection of both research institutes.

The "Intro to the 3APL Interpreter" as a prototype system was presented only on the 23rd of September 2002.

Despite it was not implemented at that time, the extension with social features is straigthforward.

An online encyclopedia about the subject denotational semantics provides the following additional information: "Within the field of computer science, there are connections [between denotational semantics] with abstract interpretation, program verification, and model checking."
This is referenced in the quoted document by the term "proof theories".

See also the quote and comment to the document titled "A Survey of Programming Languages and Platforms for Multi-Agent Systems" below.

Obviously, something happened in these 16 months from July 1998 to November 1999, which motivated the scientists to develop their 3APL agent language further, which was done in a way directly connected to the creation of our Evoos.
This happenstance becomes now explainable in this clarification and related clarifications, which implies that it was not a happenstance at all.

Taking all together, cognitive system, operational semantics, dynamics, and transition system, we have the impression of our Evoos.
In fact, we have worked out these odd similarities in relation to the fields of IAS, CAS, SAC, BDI, MAS, and FIPA, as well as ontology, which all happened exactly at the same time, when C.S. created The Proposal describing our Evoos, but not in the years before. We can also see how the authors of those very suspicious and odd documents have looked closely to take us much as possible, but also avoided any overlaps with The Proposal respectively our Evoos, which can only be done, if they knew what C.S. was creating.
We do not think that this was a coincidence in the case of 3APL, either.
Once again, our creation, research and development, and design attracted many research activities. We did not know for many years that we were so inspiring and trailblazing in this field as well.
We also note that all those other works with all these brand new things can be directley related to The Proposal, which should really make any critic think deeply about our whole case, because such a giant, deep and broad happenstance is not possible.
They have even made the same mistakes, for example in relation to references missing in The Proposal.
No, this was not a happenstance, but plain espionage and conspiracy by bad actors communicating, sharing, and collaborating at that time.

Last but not least, we also generalized the

in various ways, such as

with the creation of our Evoos and our OS.

In this relation, we quote a survey, which is about programming languages and platforms in the field of MAS and was publicated in April 2005: "A Survey of Programming Languages and Platforms for Multi-Agent Systems
[...]

MINERVA [32 [ Evolving Knowledge Bases. 2003], 33 [MINERVA - a dynamic logic programming agent architecture. 2002]] is an agent system designed to provide a common agent framework based on the strengths of Logic Programming, to allow for the combination of several existing non-monotonic knowledge representation and reasoning mechanisms. It uses MDLP and KABUL to specify agents and their behaviour. A MINERVA agent consists of several specialised, possibly concurrent, subagents performing various tasks, whose behaviour is specified in KABUL, while reading and manipulating a common knowledge base specified in MDLP.
MDLP (Multi-Dimensional Dynamic Logic Programming) is the basic knowledge representation mechanism of an agent in MINERVA. MDLP is an extension of Answer Set Programming (ASP) where knowledge is represented by logic programs arranged in an acyclic digraph.
KABUL (Knowledge And Behavior Update Language), as its recent evolution EVOLP [1 [Evolving logic programs. 2002]], is a logic-programming style language that allows the specification of updates to a knowledge base and to itself. A program in KABUL is a set of statements, each statement being a type of conditionaction rule that can be seen as encoding an agent behaviour. The epistemic effects of actions can be either an update to the knowledge base of the agent, represented by an MDLP program, or a self update to the KABUL program, thus changing the behaviour of the agent over time. Conditions range from external observations, epistemic state of the agent, as well as concurrent execution of other actions. This allows for a combination of reactive and proactive behaviour, in the sense that no external stimuli are needed to trigger the behaviour of the agent, while these can be combined with the rational features provided by the underlying MDLP knowledge representation framework and its formal and precise ASP-based semantics.

[...]

The 3APL programming language is designed so as to respect a number of software engineering and programming principles such as separation of concerns, modularity, abstraction, and reusability. It also supports the integration of Prolog ([not purely] declarative [due to procedural reading of clauses and use of constructs, that have no declarative reading]) and Java (imperative) programming languages. Interested readers will find in the 3APL user guide [...] a number of illustrative toy-problem applications such as the "blocks world", Axelrod's tournament, an English auction system, and the Contract Net protocol. 3APL has also been applied to the implementation of the high-level control of mobile robots. In particular, 3APL is being used for controlling the behaviour of SONY AIBO robots and to implement small-device mobile applications."

Comment
3APL and also MINERVA are referenced in the section Intelligent/Cognitive Agent of the webpage Links to Software of the website of OntoLinux, because unsurprisingly 3APL matches perfectly with the

{causality and statement not correct, because 3APL copied Livingstone, Aperion (Apertos (Muse)), TUNES OS, and eventually The Proposal} Also note our integration of Immobot Livingstone and 3APL, which both are based on a transition system:

In addition, as can also be seen, the figure 8 shows the components of Livingstone as a weighted graph with propabilities, which can also be used with Fuzzy Logic (FL).
our further integration of the resulting integration of Livingstone and 3APL, and the Doss TUNES OS and Aperion (Apertos (Muse)), which also provided the operational semantics of such operating systems.

See Evoos and OntoBot of OS and also Aperion (Apertos (Muse)), which is the os of the Mobile Robotic System (MRS) Sony Artificial Intelligence roBOt (AIBO) and also referenced indirectly by Evoos through TUNES OS, which has virtually the same basic characteristics like Aperion (Apertos (Muse))), and the additional properties of Evoos (e.g. meta-layer architecture), and referenced directly by OS.
Also note that Muse is also related to the field of Real-Time operating system (RTos), though in the field of Autonomous System (AS) and Robotic System (RS) many operating systems in the field of Robot operating system (Robos) and Embedded operating system (Eos) have real-time capability.
But also note the dates and keep in mind that the version of AIBO being controlled to a relatively small extent by AIBO3APL (2004) was not the first version ERS-110 of AIBO presented in 1999, which "only develops inbetween the limits of the cross-product of its hardware functionality and simulates cognitive abilities and emotions as some kind of entertaining robotics or integration of puppetry, anatomy and, mechatronics called animatronics", but at least the later version ERS-210 or even the version ERS-7 of AIBO, as documents show, which are about the RoboCup 2003, the Sony Legged Robot League, and the eXtensible Agent Behavior Specification Language (XABSL) (2002 and 2003), which again is discussed in relation to AIBO3APL.

Also, the obvious correspondence of a belief base and a database suggested to us the use of an ontology for the agent, components, and software reuse as well at the same time, and not only for the message content semantics, speech-act [speech act] communications, interoperabiltiy, service interfaces, and shared belief base of MAS.
FIPA itself specifies external behaviour, but not internal behaviour of agents, and has only reactive behaviour in the sense that external stimuli are needed to trigger the behaviour of the agent and therefore it is only a middleware for MAS specifying communication and interoperability between agents

With our OS we integrated and resolved everything by ontologics. We put it on a dynamic (hyper)graph representing the

We quote a first document, which is about a FIPA compliant Peer-to-Peer (P2P) Multi-Agent System (MAS) (P2P MAS) middleware and was publicated between the 19th to 21st of April 1999: "JADE - A FIPA-compliant agent framework
[...]

Introduction
The growth in networked information resources requires information systems that can be distributed on a network and interoperate with other systems. Such systems cannot be easily realized with traditional software technologies because of the limits of these technologies in coping with distribution and interoperability. The agent-based technologies seem be a promising answer to facilitate the realization of such systems because they were invented to cope with distribution and interoperability [6 [Software Agents. 1994]].
[...]
development environments to build agent systems (see, for example, [Reusable Task Structure-based Intelligent Network Agents (]RETSINA[)] [13 [Distributed Intelligent Agents. 1996]], MOLE [11 [Mole - A Java based Mobile Agent System. 1997]] and ZEUS [8 [ZEUS: An advanced Tool-Kit for Engineering Distributed Multi-Agent Systems. 1998]) [...] KQML [3] [...] the use of a common communication language is not enough to easily support interoperability between different agent systems. The standardization work of FIPA is in the direction to allow an easy interoperability between agent systems, because FIPA, beyond the agent communication language, specifies also the key agents necessary for the management of an agent system, the ontology necessary for the interaction between systems, and it defines also the transport level of the protocols.
[...] JADE (Java Agent DEvelopment Framework) that is a software framework to develop agent applications in compliance with the FIPA specifications for interoperable intelligent multi-agent systems. [...]

FIPA Specifications
[...] FIPA is envisaged not just as a technology for one application but as generic technologies for different application areas, and not just as independent technologies but as a set of basic technologies that can be integrated by developers to make complex systems with a high degree of interoperability.
[...]
The first output documents of FIPA, called FIPA97 specifications, specify the normative rules that allow a society of agents to inter-operate, that is effectively exist, operate and be managed. [...] Basically, it identifies the roles of some key agents necessary for the management of the platform, and specifies the agent management content language and ontology. Three key mandatory roles were identified into an agent platform. The Agent Management System (AMS) is the agent that exerts supervisory control over access to and use of the platform; it is responsible for authentication of resident agents and control of registrations. The Agent Communication Channel (ACC) is the agent that provides the path for basic contact between agents inside and outside the platform; it is the default communication method which offers a reliable, orderly and accurate message routine service; it must also support IIOP for interoperability between different agent platforms. The Directory Facilitator (DF) is the agent that provides a yellow page service to the agent platform. Notice that no restriction is given to the actual technology used for the platform implementation: e-mail based platform, CORBA based, Java multi-thread applications, ... could all be FIPA compliant implementations.
[...] the standard specifies also the Agent Communication Language (ACL). Agent communication is based on message passing, where agents communicate by formulating and sending individual messages to each other. [...]
[...]
Other parts of the FIPA standard specify other aspects, in particular the agent-software integration, agent mobility and security, ontology service, and the Human-Agent Communication. [...]

JADE
JADE (Java Agent DEvelopment Framework) is a software framework to make easier the development of agent applications in compliance with the FIPA specifications for interoperable intelligent multi-agent systems. [...]

Architecture of the Agent Platform
[...]
The software architecture is based on the coexistence of several Java Virtual Machines (VM) and communication relies on Java RMI (Remote Method Invocation) between different VMs and event signaling within a single VM. Each VM is a basic container of agents that provides a complete run time environment for agent execution and allows several agents to concurrently execute on the same host. In principle, the architecture allows also several VMs to be executed on the same host; [...]. Each agent container is a multithreaded execution environment composed of one thread for every agent plus system threads spawned by RMI runtime system for message dispatching. A special container plays the front-end role, running management agents and representing the whole platform to the outside world. [...]
Each Agent Container is an RMI server object that locally manages a set of agents. It controls the life cycle of agents by creating, suspending, resuming and killing them. Besides, it deals with all the communication aspects by dispatching incoming ACL messages, routing them according to the destination field (:receiver) and putting them into private agent message queues; for outgoing messages, instead, the Agent Container maintains enough information to look up receiver agent location and choose a suitable transport to forward the ACL message.
The agent platform provides a Graphical User Interface (GUI) for the remote management, monitoring and controlling of the status of agents, allowing, for example, to stop and restart agents. The GUI allows also to create and start the execution of an agent on a remote host, provided that an agent container is already running. The GUI itself has been implemented as an agent, called RMA (Remote Monitoring Agent). [...]

Communication sub-system
JADE front-end container maintains internally an RMI registry, used by other agent containers at bootstrap time to register themselves with the front-end, thereby joining the Agent Platform. The front-end container maintains a table of all containers along with their RMI object reference; besides, an Agent Global Descriptor Table is kept, which relates each agent name with its AMS data and with its container's RMI object reference.
[...]
JADE runtime maintains suitable agent tables and is thus able to choose the most efficient messaging mechanism, according to receiver agent location. Besides, address caching is used on each container to avoid looking up platform front end Global Agent Descriptor Table all the way. The platform presents a single interface to the outside world using standard ACC agent; this agent is a CORBA IIOP server object and listens for remote invocations. [...]

Agent execution model
A distinguishing property of a software agent is its autonomy: an agent is not limited to react to external stimuli, but is also able to start new communicative acts autonomously. This requires each agent to have an internal thread of control; however, an agent can engage multiple simultaneous conversations, besides carrying on other activities not involving message exchanges. JADE uses the Behaviour abstraction to model the tasks that an agent is able to perform and agents instantiate their behaviours according to the needs and capabilities. From a concurrent programming point of view an agent is an active object, holding inside a thread of control. [...]
[...]
JADE includes also some ready to use behaviours for the most common tasks in agent programming, such as sending and receiving messages and structuring complex tasks as aggregations of simpler ones [...]. Among the others, JADE offers also a so-called JessBehaviour that allows full integration with JESS [4 [Java Expert System Shell. 1998]], where JADE provides the shell of the agent and guarantees (where possible) the FIPA compliance, while JESS is the engine of the agent that performs all the necessary reasoning.
[...]

Conclusions
One of the first agent frameworks that consider compliance with the FIPA specifications has been here presented.
[...]
Starting from the FIPA assumption that only the external behavior of system components should be specified, while leaving the implementation details and internal architectures to agent developers, a very general agent model has been implemented that can be easily specialized to realize both reactive and BDI architectures. Moreover, the behavior abstraction of our agent model allows simple integration of external software into one of the agent tasks. The implementation of the JessBehaviour allows the usage of JESS as the agent-reasoning engine. An implementation practice that we have found useful is the usage of JESS to control the activation and de-activation of the JADE Behaviours by implementing, as a consequence, a mixed reactive-deliberative agent architecture (where JESS plays the deliberative role and the JADE behaviours play the reactive role).
[...] In particular, two applications are being prototyped in the project by using JADE. Both share the ultimate application goal of resource bundling, where one prototype analyses the television entertainment domain and the other one analyses the travel application domain. In both cases, agents are used to represent information resources, service providers and provider's interests. A middle layer of brokerage, implemented by agents, hides to the users differences between providers and helps them in locating the best ones. Finally, agents are also used to represent user preferences and to act pro-actively, on behalf of the user, to search, reserve, and possibly buy, information and services. [...]
[...]
[...]Besides, cooperative behaviour scheduling was preferred over true intra-agent multithreading because of reduced synchronization and scheduling costs: since an agent's knowledge is shared among all its behaviours, preemptive behaviour scheduling would require Java synchronized methods to be used all the way, thus paying a significant performance penalty (Java synchronized methods are about 100 times slower than ordinary methods, due to object lock management overhead [2])."

Comment
JADE was presented on The Fourth International Conference on The Practical Application of Intelligent Agents and Multi Agents (PAAM), which took place on the 19th to21st of April 1999.

As with many of the other quoted cases we have an odd timing around 1998 and 1999. Once again, our research and development, design, and creation attracted many research. We did not know for many years that we were so inspirational in this field as well.

reactive vs. deliberative, and deliberative and reactive respectively hybrid
external vs. internal
FIPA reactive and external
BDI deliberative and internal
hybrid both

We also quote a first series of slides, which is about a FIPA compliant Peer-to-Peer (P2P) Multi-Agent System (MAS) (P2P MAS) middleware and was publicated on the 9th of September 1999: "FIPA and the Internet Revolution
What is the Internet Revolution?

[...]

Future

Leading to:

Carrier Issues
Wholesale / Re-sale

[...]

[...]

Key Needs [including]

Software - how will it change?

The solutions:

Together, these lead to:

Intelligent Agents
Can be:
Small or big
Static or mobile
Smart or dumb
Long- or short-lived

Encapsulation of software 'smarts'
Autonomous components
Speech-act communications (ontology based)
Peer-to-peer (not client-server)
Glue technology/framework
Toolbox of capabilities
Collaboration / co-operation
Applications:
Negotiation (e.g. [Service Level Agreement's (]SLA's[)])
Mediation (e.g. multimedia content adaptation)
Personal assistants (e.g. Meeting Scheduling)
... anything which requires some smart assistance!

Agent Standards

FIPA - Foundation for Intelligent Physical Agents

[...]

FIPA's contributions to Agent Standards

FIPA's contributions to Agent Standards

[...]

FIPA: Current Activities

FIPA Commercialisations Barrieres

Comment
We have here the same odd timing like with JADE and other works as well: The first release of FIPA-OS was on the 31st of August 1999.
The development on FIPA-OS already ended around the year 2003 again.

We have observed such a momentum with the activities in the fields of Peer-to-Peer Computing (P2PC), Semantic (World Wide) Web (SWWW), Service-Oriented Architecture (SOA), Grid, Cloud, Edge, and Fog Computing (GCEFC), and so on around the years 2000 to 2005. One reason is that the fundings for research projects and start-ups are ending, and the requirements and interests of acting individuals are changing, but also that we gave not more to steal.
Eventually, only what we do is what remains, which shows that we were, are, and will be the driving force since more than 23 years now.

The document titled "Multiagent Network Security System using FIPA-OS" was only publicated in the year 2002.

This shows

It also raises the question if a formal model and other parts of FIPA already existed.

Can our fans and readers spot what else is related to the solutions of changing software? It is mSOA.

We also quote a second series of slides, which is about a FIPA compliant P2P MAS middleware and was publicated in the October 1999: "FIPA-OS: FIPA Everywhere!
Current Position

Potential Risks

The Challenge

Option 1: Do Nothing

Option 2: Competitive

Option 3: Collaborative/Co-operating

[...]

FIPA-OS Features

Comment
The Resource Description Framework (RDF) is "designed as a data model for metadata" respectively "is the specification for a metadata model [...] based upon the idea of making statements about resources in the form of a subject-predicate-object expression (in RDF terms, called a triple) and therefore does not imply ontology nor graph.
The emphasization of graph-based properties came with our OS in the end of October 2006.

One can see that the MAS itself already became too complex at that time, as was the case with os, Distributed C, Ubiquitous C, and so on.
Our idea was to apply the fields of operating system (os) and Agent-Based System (ABS) on themselves and also integrate them, which led to our view of the result as

our new fields of

our new foundations of

and much more,
and eventually resulted in our Ontologics and Ontologic System (OS).

As with many of the other quoted cases we have an odd timing around 1998 and 1999. Once again, our research and development, design, and creation attracted many research. We did not know for many years that we were so inspirational in this field as well.

The approach in this case was to make it as a middleware by exploiting distributed MAS in regard to Distributed os.

But it also has the many deficits like other approaches and technologies in this field.

One can also see that the other companies have taken our interest in this field due to the knowledge about our Evoos and MAS as blueprints for the development of their businesses.
Obviously, they have stolen our Evoos and OS to continue with that mess already started around 1999 or even earlier.

We also quote a third series of slides, which is about a FIPA compliant P2P MAS middleware: "FIPA-OS Agent Tasks
[...]

What is a task?

Why use tasks?

What types of task are there?

What types of task are there?

How do I use tasks?

[...]

How do I use tasks?

How do I use tasks?

More information

Comment
Surprisingly or not, for the other 2 presentations quoted before at least the month and year of their publication is given.

This sounds a little like mSOA, which raises certain questions. Howsoever, there is certain difference, because , but we have IPC, os-level virtualization or containerization, and Resource-OC, which are the foundations of mSOA, while task of an agent is not.

Having said this, here a section of a red line is drawn.

We also quote a fourth series of slides, which is about a FIPA compliant P2P MAS middleware and was publicated on the 21st of March 2000: "FIPA-OS [] [...] FIPA Agent Platform
[...]

FIPA: Situation now

[...]

FIPA-OS

• Imperial College have this almost ready

[...]

Example application: FACTS

• Problem: book a video conference meeting
• Answer: give task to your Personal Communication Agent, and stop worrying!

VPN scenario agents

• FIPA platform agents + Timer Agent
• Wrapper Agents: Microsoft Outlook Wrapper Agent (OWA) and Microsoft NetMeeting Wrapper Agent (NMWA)
• Personal Communication Agent (PCA)
• Service Provider Agent (SPA)
• Network Provider Agent (NPA)

[...]

User preferences

• frame rate (high/medium/slow)
• meeting time (morning/afternoon/not lunchtime)
• maximum cost of meeting

PCA - SPA negotiation

• frame rate
• frame size
• price
• penalties for failing the service

SPA - NPA negotiation

• price
• penalties for failing the service
• bandwith
• availability

Commissioning the meeting

Extending the contract

• extending the meeting time
• adding new participants
• time and/or iterations must be limited - end users are waiting for the change to occur
• some issues are not negotiable

Conversation Problem

• may messages sent and being replied
• how to know into what conversation they belong?

• individual ACL messages, converstation-ids
• implementation complicated

Option 2: Conversation Management [(CM)]

• if old, add message into converstion to correct place in the protocol
• if new, create a new conversation

Conversation Management

• adds new messages to the list
• adds messages that are part of existing conversations to the correct object in the list

Planner Scheduler [(PS)]

• routes "sent" messages out of the CM through agent comms to the appropriate agent

• PS removes finished converstion objects from conversations-in-progress to the list

Future: Parser Factory [(PF)]

[...]"

Comment
Our OS takes a graph as syntax neutral generic content object.

We also quote a fifth series of slides, which is about a FIPA compliant P2P MAS middleware and was publicated on the 14th of August 2001: "FIPA & FIPA-OS
[...]

The FIPA type of agent

• Social ability: agents can communicate & collaborate
• Autonomy: agents can say no (can also be commanded)
• Reactive: agents perceive the environment & respond in a timely fashion
• Pro-active [Proactive]: agents are goal-directed, they can take the initiative.

• supported by mentalistic models of communication

[...]

FIPA focuses on speech act protocols, dialogues & ontologies
[...]

FIPA: What's in a Name?

• software agents but initial vision was physical agents (robotics)
• specifying communication and interoperability between agents
• specifies external behaviour not internal behaviour - don't specify how agents process and reason about the information they receive.
• Use in heterogeneous environments

What is standardized?
[...]
agent part [] knowledge [] reasoning, task model [] learning
agent subpart [] [...] ontology

What is standardized? (2)

• Dialogues, communication primitives or speech acts, content (actions), ontologies

• (Set by the choice of speech act & dialogue)
• P2p, client-server, manager-contractor

• Core: Transport (encodings), Directory, Naming
• Other: ontology, mobility, nomadicity, etc

• MAS & MMAS: Platforms, Domains, Abs. Arch.

Models, Representation & Verification

• Standard needs to be verifiable
• Conformance to the standard needs to be verifiable

• Formal Models (design)
  • can be verified using logic proofs
  • but can't easily verify complexity of implementation

• Well-established mapping of design to implementation
• Verify implementation at specified points

[...]

Content is defined using a (ontology) language & a (domain) ontology
[...]

A frame-based ontology example: a FIPA management ontology (part)
[...]

Abstract Architecture & the service model

• ACL, message transport directory
• Services don't have to be agents but they can be

• agent-platform, gateways, bootstrapping, agent configuration and coordination.
• These elements are not included in the abstract architecture because they are implementation specific. Some elements will exist only in specific instantiations.
• [...]

[...]

Abstract architecture vs. Agent Platform

• FIPA00023 agent management specification
• FIPA00067 message transport specification

[...]

FIPA-OS

FIPA-OS is the first Open Source implementation of FIPA

The core types of agent behaviour supported by FIPA-OS

• Reactive: can react to ACL messages from other agents in the environment
• Proactive: they can decide when to initiate interaction with other agents
  • N.B. simple goals. E.g., register with the name service, without plans
• Social: (see reactive and proactive)
• Autonomous: each agent has multiple threads of control
• Mentalistic features: via use of ACL"

[...]

FIPA-OS: Task Manager

[...]

Specifying the agent architecture, organisation & roles
Determined by

• Conversation patterns used
• The middle agent hierarchy depth
• Platform & Service interlinking

• service discovery: uses a 3 tier client server arch. & the fipa-request conversation pattern
• service usage: uses a 2 tier client server arch. & fipa-request conversation pattern

Interlinking or Federating Agent Platforms
[...]

Developing agents & services

• Use the standard FIPA agent management ontology

• Define a domain-specific ontology

Using Ontologies
Ontology language
Repository, Document, Environment representation of Domain Ontology: RDBMS, XML/RDF
Agent internal representation of Domain Ontology, e.g., ... Java objects
Communication representation of Domain Ontology: XML/RDF

[...]

An agent consists of objects but it is more than a set of objects

Content languages vs. ontologies
Content language [] Ontology language? [vs.] Ontology [] domain instance ontology

Comment
In the comment to the quoted document titled "Agent Programming in 3APL", we already noted that mentalistic features via use of ACL respectively considerable limitations of intentionality as a basis for defining respectively using mental attitudes to formalise and for standardising the semantics of an ACL, including the FIPA ACL.

This series of slides was publicated after the ontology-based paradigm got a considerable momentum. The first FIPA 97 and FIPA 98 specifications were not elaborated to this extent, specifically in relation to the use of ontologies, which was only focused on speach-act communication and messaging.
An unrelated work is titled "A Framework for Multi-Agent Belief Revision Part I: The Role of Ontology", was publicated between the 6th to 10th of December 1999, and uses ontologies for Multi-Agent Belief Revision (MABR) on the basis of agent messaging and communication. But according to its list of references this work was neither related to the BDI agent architecture nor the FIPA standard before the publication of our Evoos, and only connected with BDI and FIPA in the following years (see for example the related master's thesis titled "A Framework for Multi-Agent Belief Revision" and presented in August of 2002).
Also note that in relation to ontology the focus was laid on the mediation and alignment of different knowledge bases at that time, but had nothing in common with any HardBionic (HB) and SoftBionic (SB) models.
Also note that TUNES OS (Arrow System), Evoos, and Askemos are also related to the data structure of Artificial Intelligence (AI), which is a set of associations from properties to values and is called frame.

We also quote a sixth series of slides, which is about FIPA-OS and was publicated on the 7th of February 2002: "FIPA-OS
[...]

Additional Agent Functionality

• Tool Independent (Architectural design will remain so)
  - OIL, DAML, DAML+OIL, DAML-S (Committed to supporting these languages)

• FaCT (Relational inference over D-Logic encoding)
  - DAML+OIL
• JENA (Inference over explicitly defined subsumption of ? properties)
  - DAML-S

Knowledge Bases

• Model modification
• Model querying"

Comment
That was already in the year 2002.

We quote a document, which is about Agent Communication Language (ACL) in relation to the BDI agent architecture, the FIPA specification, and the JADE and was publicated in the year 1999: "A Protocol-Based Semantics for FIPA'97 ACL and its Implementation in JADE
Abstract
There are fundamental limitations on using mental attitudes to formalise the semantics of an Agent Communication Language (ACL). In this paper, we define a general semantic framework for a class of ACLs in terms of protocols, and develop a method for designing and specifying a member of this class, and configuring it for a particular application. [...]

Introduction
[...] From the computing perspective, agents are autonomous, asynchronous, communicative, distributed and possibly mobile processes. From the AI perspective, they are communicative, intelligent, rational, and possibly intentional entities.
[...]
[...] Therefore there are considerable limitations of intentionality as a basis for defining the semantics of an ACL (see also [Singh, 1998]).

A General Semantic Framework for ACLs
[...]
We would argue that there are three layers of semantics here:
1. The content level semantics, which is concerned with understanding the content of a message (i.e. the content meaning), and is internal to an agent;
2. The action level semantics, which is concerned with replying in appropriate ways to received messages, and is external to the agents;
3. The intentional semantics, which is concerned with making a communication and how to react to a received message (i.e. the representational meaning), and again is internal to the agent.
We would argue that the current FIPA ACL semantics [FIPA, 1997], for example, is level 3, and because it is internal to an agent, its usefulness in standardisation has been questioned [Wooldridge, 1998; Singh, 1998]. The only part of the communication that is amenable to standardisation is the observable tip of the iceberg: namely the communication itself. Note that this properly includes ontologies, so that there may be a 'standard interpretation', but the actual interpretation of the content of the message is once again internal to the agent.
We are therefore inclined (as with many others [...]) to take a more protocol-oriented view of the ACL semantics. From this point of view, the communication between two (or more) agents can be viewed as a conversation. Individual speech acts therefore take place in the context of a conversation. We then specify the meaning of performatives by describing an inputoutput relationship. In our case, we define the meaning of a speech act (as input) to be the intention to perform another speech act (as output). The performative used in the output speech act will be determined and constrained by the context (i.e. the conversation state) in which the input speech act occurred, while the content of the output speech act will be determined by the context (the agent's information state) in which the input was received.
[...]

A Method for Designing ACLs
The definition of a particular ACL by a 3-tuple <Perf, Prot, reply> effectively defines a set of finite state diagrams with arcs labeled by performatives and states labeled by which agent's turn it is to 'speak' in the conversation. As we show in the next section, it is relatively straightforward to do this for, for example, the FIPA ACL [...]. [...]
[...]

A Protocol Semantics for FIPA'97 ACL
[...]
[...] In the EU GOAL project, where we developed a agent-based system for distributed document review, the deadline could be days [Pitt et al, 1996]. However, in the EU MARINER project, where we are developing an agent-based system for load control in Intelligent Network, the deadline was seconds [Pitt and Prouskas, 1998]. However, the same basic pattern of interaction was common to both systems.
[...]
[...] Here we complete the specification in terms of A BDI (Beliefs-Desires-Intentions) agent architecture [Kinny et al, 1995; Pitt and Mamdani, 1999].
[...]

Implementation in JADE
[...] JADE can then be considered an agent middle-ware [middleware] that implements an Agent Platform and a development framework. It deals with all those aspects that are not peculiar of the agent internals and that are independent of the applications, such as message transport, encoding and parsing, or agent life-cycle. The JADE features and its internal architecture are described in [Bellifemine et al [JADE - A FIPA-compliant agent framework], 1999].
[...]
Reconsidering the theoretical framework, JADE fixes the 3-tuple and does not allow an agent to extend it and create new dialects. In fact, as pointed in section 3, this 3-tuple is the necessary standard ACL: (1) the Perf set is fixed because if an agent tries to send a performative that does not belong to the FIPA set, an exception is thrown; (2) in a certain sense, also the Prot set is fixed because JADE provides behaviours only for those interaction protocols that have been implemented by FIPA. Of course, the programmer is still free to extend this set by implementing new protocols. It is under consideration also the implementation of a new feature that would allow an agent to construct at run-time a new protocol; and (3) for each element of the Prot set implemented by JADE, the reply function is also fixed and cannot be changed by the programmer.
[...]

Conclusions and Further Work
[...] The FIPA standardization body has produced a specification of a syntax and semantics for a 'standard' ACL to address the essential requirement for communication between agents. This is loosely based on speech act theory, and defined in terms of a set of performatives or communicative acts. A communicative act occurs whenever one agent sends a message to another.
At present, though, it is not clear whether the specification of the formal semantics is intended to be normative or informative [...].
We therefore contend that the formal semantics is too strict to be normative, in that intentional conditions on the performance of speech acts do not generalise across all agents and all applications. While well-motivated under certain assumptions (e.g. co-operation), it can at best provide only informative guidelines to an agent developer.
Our approach allows us instead to identify a class of ACLs. We envisage starting from a set of core performatives and protocols (i.e. performatives whose intended meaning is intuitively clear and protocols from some common functions). This would be the 'standard' ACL, the root of the class of ACLs, which could then be extended within the same semantic framework for particular applications. [...] There is then a direct mapping into the implementation model of JADE [...].

Acknowledgements
This work has been undertaken in the context of the EU ACTS Projects FACTS (AC317) and MARINER (AC333), and UK-EPSRC/Nortel Networks funded Project CASBAh (Common Agent Service Brokering Architecture, EPSRC Grant No. GR/L34440), and support from these funding bodies is gratefully acknowledged."

Comment
Before the first FIPA-complaint agent platforms were implemented, the first foundational deficits were shown and solved, which once again shows how new everything in this specific field of agent programming languages, frameworks, and middleware was in 1999 and like in the cases of the fields of Soft Computing (SC) and ontology-based systems momentum increased considerably after several years of disinterest. But all these fields and others are the foundation of our Evoos, which was the only new thing.

Also very interesting are the sources of funding.

We quote a second document, which is about a FIPA compliant P2P MAS middleware and was publicated in the April 2000: "The FIPA-OS agent platform: Open Source for Open Standards
Abstract
[...] FIPA-OS is being deployed in several domains including virtual private network provisioning, distributed meeting scheduling and a virtual home environment. [...]

Introduction
In Multi-Agent Systems (MAS), heterogeneous distributed services are represented as autonomous software agents which interact using an Agent Communication Language or ACL based on speech acts ([... Speech Acts], 1969). [...]
[...] the development, maintenance and management of distributed software services based on the MAS paradigm introduces new complexity [...].

FIPA and other agent standards
In the context of FIPA, an agent^1 is an encapsulated software entity with its own state, behavior, thread of control, and an ability to interact and communicate with other entities - including people, other agents, and legacy systems. This definition puts an agent in the same family as objects, functions, processes, and daemons but it is also distinct in that it is at a much higher-level of abstraction. The agent interaction paradigm differs from the traditional client-server approach: agents can interact on a peer-to-peer level, mediating, collaborating, and co-operating to achieve their goals.
A common (but by no means necessary) attribute of an agent is an ability to migrate seamlessly from one platform to another whilst retaining state information, a mobile agent. One use of mobility is in the deployment and upgrade of an agent.
Another common type of agent is the intelligent agent, one that exhibits 'smart' behavior. Such 'smarts' can range from the primitive behavior achieved through following user-defined scripts, to the adaptive behavior of neural networks or other heuristic techniques. In general, intelligent agents are not mobile [...]. There is an exception to this last statement, 'Swarm' intelligence. This is a form of distributed artificial intelligence modeled on ant-like collective intelligence. [...]
Another prevalent, but optional, attribute of an agent is anthropomorphism or the 'human factor': this can take the form of physical appearance, or human attributes such as goal-directed behavior, trust, beliefs, desires and even emotions.
There are three important agent standardization efforts which are attempting to support interoperability between agents on different types of agent platform: KQML community, OMG's MASIF and FIPA.
Of these three: KQML and FIPA both define interaction in terms of an Agent Communication Language (ACL) whereas MASIF defines interaction in terms of Remote Procedure Calls (RPC) or Remote Method Invocation (RMI). In contrast to the traditional RPC-based paradigm, the ACL as defined by FIPA provides an attempt at a universal message-oriented communication language. [...] Although there are several hundred verbs in English, which correspond to performatives, the ACL defines what is considered to be the minimal set for agent communication (FIPA ACL consists of 20 or so performatives). [...]

[...]

FIPA
[...]
The Directory Facilitator (DF), Agent Management System (AMS), Agent Communication Channel (ACC) and Internal Platform Message Transport (IPMT) form what are termed the Agent Platform (AP). The DF provides "yellow pages" services to other agents. The AMS provides white-page services and life-cycle [lifecycle] management services for agents and the ACC supports inter-agent communication. The ACC supports interoperability both within and across different platforms. The Internal Platform Message Transport (IPMT) provides a message forwarding service for agents on a particular platform, which must be reliable, orderly ([... FIPA - towards a standard for software agents], 1998).
[...]
To be minimally FIPA compliant requires compliance [with] the Agent Management FIPA specification, the Agent Communication Language FIPA specification and the [non-agent] software-agent integration specification. [...]
The FIPA standards in some areas introduce conceptual problems for designers and implementers. For example, the FIPA ACL (Agent Communication Language) focuses on an internal agent mental agency of beliefs, desires and intentions and closure is not enforced (agents are not compelled to answer) - these may hinder multi-agent co-ordination ([... Evaluating the FIPA standards and its role in achieving cooperation in multi-agent systems], 2000).
The FIPA normative specifications are also not intended to be a complete blueprint or specification for building multi-agent system. For example, FIPA standards do not prescribe how to describe existential aspects of how agents in a discrete world [...].
[...] currently, agents are managed via a message-passing interface at the ACL level, i.e., agents are managed by interaction with the three core FIPA platform agents: the DF agent, the AMS agent and the ACC agent.

[...]

Related Work
Most agent platforms [...] naturally offer openness at the agent level in that although the platform itself may be fixed or closed, service and user agents can be dynamically added to the platform and agents themselves are naturally co-operative. This requires a common means of representing [encoding], understanding [ontology] and exchanging [protocol] service information. FIPA platforms defines a standard base protocol based on speech acts[,] several standard ontologies: a core one for registering and querying de-registering services [part of the FIPA management ontology] and various domain specific ones. No specific service encoding is mandatory. Non-FIPA platforms require the use of platform specific service ontology, encoding, and protocol combinations.
[...]
[...] FIPA-OS initially focuses on providing abstractions and interfaces (APIs) for developers who wish to extend, enhance and integrate an agent platform with existing software infrastructures.
[...]

[...]

User requirements and System Design
FIPA-OS description
[...]

The FIPA-OS architecture can be envisaged as a non-strict layered model (Figure 3). In a nonstrict layered model, entities in non-adjacent layers can access each other directly. The developer is able to extend the architecture not only by appending value-added layers such as specialist service agents or facilitator agents on top but in addition, lower or mid layers can be replaced, modified or deleted.

[...]

Multi-tiered ACL Communication
[...]
As ACL communication is so rich, it is often represented as a multi-tiered layer in its own right ([... KQML as an agent communication language], 1997). FIPA-OS supports ACL communication using four sub-layers of components: conversation, ACL message, content (syntax) and ontology (content semantics).
[...] FIPA-RDF specification for encoding the content in XML[.]
[...] [Internet InterOrb Protocol (]IIOP[)] as the baseline transport protocols as specified by the FIPA agent architecture [...]. The name and directory services supported by the AMS and DF respectively are implemented using the [Common Object Request Broker Architecture (]CORBA[)] name services. To support cross-platform access, gents are accessed using CORBA IOR (Interoperability Object References) object references which are stored as HTML-encoded strings on a Web server. In addition, Java RMI may also be used as an IPTM protocol.

[...]

Configuration
There is built-in support to plug in different types of components at key interfaces or hot-spots. These include: comms or transport, message content encoding and the type of ACL message storage. These are plugged in during platform initialization. Developers can design and implement additional types for these plug-and-play components. The plug-in nature of the platform supports a dynamic component-oriented middleware model and promotes a combination of thin client agents and thin platform services. [...]
The types for these plug-and-play components are currently configured in terms of profiles for the platform as a whole, and for each individual agent. These profiles are encoded in XML/RDF and stored in resource files. [...]

[...]

Evaluation
[...]
FIPA-OS is being used in several other projects and application domains. These include: a virtual home environment concepts and future wireless services project called CAMELEON ([...], 1999), personalized retrieval of information found in the retail sector in a project called MAPPA [...] and in a research project on agents infrastructures called CASBAH ([... Agent-oriented middleware for integrating customer network services], 1999).

Future work and conclusions
Future work
[...]
changes to the transport service (the ACC service is no longer performed by an agent) will be required. FIPA is also considering removing the mandatory use of an ACL "agentized" interface to the platform directory and management services.
[...]"

Comment
We note that we are talking about autonomous software agents (softbot), specifically agent-based middleware and platforms, and applications, but not Model-Based Autonomous System (MBAS) or Immobile Robotic System (ImRS or Immobot). These becomes even more obvious in relation to the use of ontologies. In fact, FIPA only uses frame-based ontologies and an ontology service for the lifecycle management, the service interfaces, and the speech-act [speech act] communications of agents, and the domains respectively semantics of the message contents for interoperabiltiy.
See also FIPA frame-based core ontology for life-cycle management and domain ontologies for communication services of agents, and domain and content semantics.
FIPA only uses ontologies for the life-cycle management and communication services for agents and the domains respectively contents of the messages.
"FIPA platforms defines a standard base protocol based on speech acts[,] several standard ontologies: a core one for registering and querying de-registering services [part of the FIPA management ontology] and various domain specific ones. No specific service encoding is mandatory. Non-FIPA platforms require the use of platform specific service ontology, encoding, and protocol combinations."
In contrast, our Evoos has an ontology for everything and therefore defines the Ontologic Agent (OntoAgent) and Ontologic roBot (OntoBot).

New complexity is introduced with the management of communications networks and the development, maintenance and management of distributed software services in general and of services based on the MAS paradigm in particular. The foundational systems and platforms are more and more complex, which led to our Autonomic Computing (AC) and Resource-Oriented Computing (ROC) paradigms first envisioned, created, presented, and discussed with our Evoos.

Obviously, the statement about mobile intelligent agents is wrong, because

As with other funtionality, we also have asynchronous funtionality moved from the application layer and middleware layer into the kernel süace. This and the evidences, which prove that our Ontologic System (OS) was taken as source of inspiration and blueprint, show once again why the related functionality in (certain) operating system kernels are illegal, like for example Linux.

FIPA does not focus on an internal agent mental agency of beliefs, desires and intentions, because "FIPA platforms defines a standard base protocol based on speech acts", but "FIPA standards do not prescribe how to describe existential aspects of how agents in a discrete world", and a speech act is more general. Entities wanted to suggest that FIPA is based on the Belief-Desire-Intention (BDI) agent architecture extended with social agency.
We have discussed the issue with mentalistic features via use of ACL in comments of quotes above.

Extremely interesting is the fact that the Resource Description Format (RDF) is only used for encoding, but not as foundational processing or computing model, and as part of the speech acts. Obviously, it has not been understood that an RDF triple is also viewed as subject, predicate, object. In contrast, our Evoos has this integrated through the TUNES OS based on the Arrow System (AS) and Arrow Logic (AL), and our Ontologic System (OS) has this extended much further and even generalized completely over main sentences and Natural Language Processing (NLP) and Natural Image Processing (NIP) to Natural Mutlimodal Processing (NMP).

We also note no blackboard systems with the exception of systems of loosely-coupled applications and services, and no Jini, while Jini had no ontology. In contrast, our Evoos has all the foundations and our OS has it all.

One can see how the FIPA tried to make it like our Evoos.
Somehow, we have the impression that the authors have taken our Evoos as blueprint, which is supported for example in the chapter about future work to make FIPA more like an operating system (os) by removing platform service agents and "agentized" interfaces. At this point, we always explain that we have improved the whole system stack by moving agent functionality from the user space or application layer, and the middleware layer into the kernel space or operating system layer.

We quote a document, which is about the field of Intelligent Networking (IN) and was publicated on the 31st of August 1999: "FIPA-compliant agents for real-time control of Intelligent Network traffic

Abstract
Autonomy, adaptability, scalability, and flexible communications are all attributes of agents and multi-agent systems which suggest that they may offer timely solutions for dealing with the growing complexity of the tasks of traffic control and resource management in telecommunications networks. However, if agent-based solutions to network management problems are to be successful then it will be important that heterogeneous agents and agent platforms inter-operate in accordance with internationally accepted standards. Although standards of this nature are being developed, they are not tailored specifically to the needs of the telecommunications domain, with the result that important issues, such as support for the operation of agent systems in real-time constrained environments, do not seem to be adequately addressed. We present two agent-based systems for control of traffic load and resource allocation in Intelligent Networks. One of these strategies is based on the concepts of 'Market-based Control', the other on the concepts of 'Ant Colony Optimisation' [of Artificial Life (AL)]. Using the market-based strategy as an example we show that enhancements to existing FIPA specifications would be required to implement these strategies in order to satisfy their real-time operation constraints. We also suggest a number of potential enhancements to FIPA specifications that would alleviate some of the identified problems."

Comment
odd are the timing and the ant colony optimisation of AL

FIPA is reactive agent architecture, but not deliberative agent architecture and therefore not hybrid agent architecture
no BDI (no BDI with Model-Based Autonomous System (MBAS) or Immobile Robotic System (ImRS or Immobot)(?!), and Intelligent Environment (IE)(!?)),
no Holonic Agent System (HAS)
We do remember this point when creating and developing Evoos, because real-time was considered fantasy in the field of deliberative agent architecture, such as BDI, and Cognitive Agent System (CAS), such as SOAR, but the fields of Distributed operating system (Dos) and Robotic operating system (Ros) (not to be confused with Robot operating system (Robos)) include operating systems of the field of Real-Time operating system (RTos), such as Aperion (Apertos (Muse)).
In the OntoLix and OntoLinux Website update of the 16th of May 2016 we already said in relation to agent-based or -oriented os that we considered to use a subsymbolic scheduler for action planning.
Here computing and networking devices ... we already viewed Evoos as Immobot and cybernetic self-reflection, self-augmentation, and self-extension of C.S. and a user.

Also note, that this is neither Cyper-Physical System (CPS) nor Autonomic Computing (AC), but simply control and management based on interoperable MAS.

We quote once again an online encyclopedia about the subject Autonomic Computing (AC) or Autonomic System (AS): "Autonomic computing (AC) is distributed computing resources with self-managing characteristics, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. Initiated by IBM in 2001 C.S. in 1999, this initiative ultimately aimed to develop computer systems capable of self-management, to overcome the rapidly growing complexity of computing systems management, and to reduce the barrier that complexity poses to further growth.[1 [The Vision of Autonomic Computing. 2003 A Vision of Autonomic Computing. 6th - 8th of November 2002]]"
We also quote an online encyclopedia about the subject Autonomic Networking (AN): "Autonomic Networking follows the concept of Autonomic Computing, an initiative started by IBM in 2001 C.S. in 1999. Its ultimate aim is to create self-managing networks to overcome the rapidly growing complexity of the Internet and other networks [(also called Intelligent Network (IN))] and to enable their further growth, far beyond the size of today.
We also quote the summary of the cited document titled "A Vision of Autonomic Computing" and publicated in 2002: "A Vision of Autonomic Computing
[...]
Autonomic Computing is a new approach to coping with the rapidly growing complexity of operating and integrating computing systems. This paper elaborates on the vision of autonomic, or self-managing computing."
The latter also cites the documents titled "Ant colony optimization: A new meta-heuristic. In 1999 Congress on Evolutionary Computation" and publicated in 1999, "A market-oriented programming environment and its application to distributed multicommodity flow problems" publicated in 1993, and "Evolving globally synchronized cellular automata" publicated in 1995, which proves once again

and the documents titled "The Semantic Web" and publicated in May 2001, and "Foundation for Intelligent Physical Agents", which proves once again

We quote a document, which is about the field of Intelligent Networking (IN) and was publicated in 2000: "IN Load Control Using a Competitive Market-Based Multi-agent System

Abstract
Intelligent Networks (IN) are used in telecommunication networks to provide services that require a decision-making network element. This element is the Service Control Point (SCP). An overload of an SCP can result in a great reduction in the Quality of Service (QoS) provided by the IN. While traditional IN load control algorithms assume a single service network model or make use of a centralized controller, in this paper we propose and investigate a market-based model for solving the distributed IN load control problem for a multi-service network, where any service can be provided by any SCP. Furthermore, we study a realization of this model based on a multi-agent system (MAS) and finally draw conclusions as to both its efficiency and effectiveness."

Comment
This time the authors came too late with their attempt to rescue the older work by curing its deficits, specifically by generalizing it to a multi-service network model and decentralization respectively using a decentralized controller or distributed controller.
And that is the general problem with virtually if not all quoted works, which shows

We quote a chapter of a document, which is about securtiy and trust in relation to FIPA, and was publicated in June 2000: "Towards Improved trust and security in FIPA agent platforms
[...]

3 Current FIPA Security and Trust Models
Although, specifications pertaining to security within the context of the FIPA specifications were started at the beginning of 1998, the FIPA 98 agent management specification [10] and the FIPA 98 agent management security specification [11], there is still no coherent, completed picture for agent security within FIPA at this time. In fact both of these specifications have now been declared obsolete by FIPA - the management specification has been superseded by new specification but which contains no references to security. Nevertheless, we use these as a reference point for our discussion on FIPA security below because they represent FIPA's last published viewpoint on security.
Before we discuss these particular FIPA specifications in detail, it is worth considering why there are no completed current specifications for agent security within FIPA. This is perhaps related to a more general question of whether a generic or default level of agent security ought to be specified that can be applied to different types of agent infrastructures and application domains.
These discourse can be summarized as:

Let us debate some of these points in more detail. The generic forces for security engineering are different from other types of engineering such as application development."

Comment
At that time,

were not possible at all due to the deficits of basics respectively low state-of-the-art and as in the case of Massively Multiplayer Online Game (MMOG) not solved, but our OS does.

But fault tolerance in the field of Distributed operating system (Dos) (see TUNES OS and Aperion (Apertos (Muse))) and smart contract transaction protocol with Peer-to-Peer (P2P) (see once again Dos TUNES OS and also MAS FIPA (see also JADE)), os-level Virtual Machine (VM) integrated with or based on Artificial Neural Network (ANN) (see also P2P VM Askemos), later our integration of the blockchain technique and the digital ledger technology, specifically our Distributed Ledger Technology (DLT), in addtition to capability-based os.

We quote a document, which is about the field of multimedia system and was publicated in July 1998: "Intelligent multi-modal systems, Smart Work Manager
[...]

Development Platform: Parallel Virtual Machine
The core of the platform for building the Smart Work Manager is the Parallel Virtual Machine (PVM) (Geist, 1994) software package. PVM enables a collection of heterogeneous computers to be used as a coherent and flexible concurrent computational resource. [...]
PVM uses sockets for inter-process communications but this mechanism is transparent to the user. It essentially provides a computational space where processes (i.e. programs) can run and communicate. In particular, processes can spawn other processes, send messages to other processes, broadcast a message to a group of processes or kill other processes.
[...]

[to be continued]"

Comment
The authors also publicated the documents "The Intelligent Assistant: An Overview" and "Intelligent Multimodal User Interface" in 2000, which are two of the exemplary works, where authors tried to rescue, cure their older works.
In this case we wondered, because the quoted document about SWM and the document about Intelligent MMUI have virtually the same abstract and introduction. Therefore, we asked us what was changed or added in case of them and also in case of the Intelligent Assistant (IA), the Parallel Virtual Machine (PVM) was substituted with Peer-to-Peer (P2P) FIPA-compliant MAS ZEUS, potentially due to Evoos.
We also wondered why Intelligent Agent and Smart Work Manager were connected with each other. cheap trick

task vs. goal, plan, belief, ... (see for example the quoted document titled "Agent Programming in 3APL" and task scheduling vs. goal or belief revision)

are already complex themselves.

Also note that the Parallel Virtual Machine (PVM) is only used for Distributed Computing (DC), or better said High-Performance Computing (HPC), but neither for Peer-to-Peer (P2P) Computing (P2PC) nor for os-level virtualization or containerization. While the former has been hastily cured with an updated document, the latter feature of our Evoos was just not understood in general and in relation to Service-Oriented Architecture (SOA) in particular, because this was also viewed as another subject for frameworks and middleware.
But as we always explain, designing the wrong layers costs a lot of performance and other advantages. In this case, we have a lot of , as is the case with asynchronous communication or messaging, which we have also solved (simultaneously) with our integration Ontologic System Architecture (OSA) and exceptionless (asynchronous) ....

At least one author is also known by us, because we wondered about his relation with the other author of the quoted document "JADE ..." in particular and P2P middleware and other properties of FIPA and JADE, that showed us who is responsible for this fraud.

In this context, we also know "a multi-blackboard platform with ontology-based messaging" "based on [the Parallel Virtual Machine (]PVM[)]" publicated in 2003.

Also very interestingly, we got more insights in relation to

We quote a document, which is about the fields of mutlimedia and Mobile Computing (MC), and was publicated in July 1998: "Agents, mobility and multimedia information

Abstract
This paper describes the design philosophy and implementation of a system which manages the location, retrieval and processing of multimedia information for mobile customers. The system uses intelligent agents in all aspects of management and allocation of service components to perform the most appropriate translation and movement of information through the network.
The agents use an open market model to provide the services. The strategy of the management agents is to stimulate demand to use their services, which is offset by quality-of-service factors, leading to balanced utilisation of the network. Agents also act as proxies for the user to take into account personal preferences.

1. Introduction
Multimedia services present a telecommunications provider with many interesting options when compared to traditional voice telephony. The latter is based around a single, standard offering over which universal and common services are provided. It is questionable whether such an approach is either desirable or obtainable with multimedia. A plethora of sources and displays are available, varying from phones and video cameras that plug into telephone networks, to top-end workstations capable of displaying pseudo-3D virtual reality graphics. When users want to send multimedia documents they will have to give a great deal of thought to what equipment the receiver has available to display the document, and to the ability of the network to deliver it at a reasonable transmission rate. This complexity will inhibit the use of such services. Simply, users want to send documents and leave the 'network' to make intelligent choices about where and how to process, translate and display them.
These problems are particularly acute for the mobile user. Mobile equipment rarely has the display or processing power of 'fixed' items due to the need for low-power consumption and robust build quality. Thus the mobile user will only have access to lower processing power terminal facilities. Additionally, the mobile customer is normally attached to the network via a wireless tail which normally has less bandwidth than the fixed network. In the future the situation will almost certainly become more complex with users able to access information over an enormous number of different networks each of which will have different advantages and limitations.
The provision of intelligent management systems that select the optimum format of reception and the best network for the purpose and cost will be essential to give customers the necessary flexibility, and network operators a competitive edge. The network should be capable of taking information and delivering it to the user in the most appropriate form, depending on the equipment in the vicinity and the user's personal preferences. It is this problem that has been addressed using intelligent agents to provide the flexibility for a solution that is:

[...] agent approach used in the Multimedia Information Interchange (MII) system [...]

2. Design Philosophy and Architecture
For the purposes of this paper, an intelligent agent is an autonomous computer program that continuously learns about its environment, adjusts its behaviour appropriately and negotiates with other agents to buy and sell resources needed for the overall system to undertake some task. Autonomy, learning and negotiation are the basic attributes of this agent definition [1].
A number of key ideas about what agents are and how agent systems should function drove the design of the agent model and architecture. As can be seen from the list below, the three major criteria in designing an agent-based network and service management system [2] are scalability, flexibility and robustness:

Comment
robust in the sense of fault-tolerant in regard to components
os, Dos, mSOA are also flexible, but no connection here

In addition, we knew that resilience was a field, which was not done properly totally neglected by operating system and also middleware a huge lack of security, and here we could see it as well with agent systems no security.
"Deriving consensus in multiagent systems"

Conclusion

The approach in the case of FIPA-compliant MAS, including FIPA-OS and JADE, was to make it as a middleware by exploiting MAS based on distribution and parallel processing in regard to Distributed os.

but also has the many deficits

Do not confuse "agent management content language and ontology", which are focused on the Agent Communication Language (ACL), with the model-based, and the agent model and ontology, which are focused on the design / modelling and structure, and operation.

Agent Communication Language (ACL)
We noted at that time that despite the document titled "Active Databases and Agent Systems: A Comparison" and about BDI, and an "ontology [refers to the] content of the information in a database irrespective of the media type" according to three layer architectures consisting of

or

ontology merely is used for the content of a message, speech act as part of an ACL and in one case for Multi-Agent Belief Revision (MABR), but in both cases only for reliable agent communication and system interoperability, {frame-based ontology: FIPA management ontology} but not in relation to Software Engineering (SAE) for software reuse and agent, and formal semantics is used in some few cases of agent programming languages, frameworks, and systems. There was no overall integration as shown with our

respectively themselves.

Searle J: "Is the brain's mind a computer program? no: a program merely manipulates symbols whereas a brain attaches meaning to them". 1990.
Evoos is based on a "Metaschichtenarchitektur, die ein Software-System mit einem zugehörigem Metasystem in Beziehung setzt==meta-layer architecture, which relates a software system to an associated meta-system" and TUNES OS, which also implies Aperion (Apertos (Muse)) with object-level and meta-level, both levels and their relations with ontology in case of our Evoos.

But this overall approach and this overall architecture are foundational and elemental for the successor of the Internet and the World Wide Web (WWW), specifically for validation and verification, and resilience, but also for system, application, and service development and much more.

As we already said several times in the past and one can also see, the whole thing, including all the single fields and works, have been created, reworked, improved, integrated, rearranged, reinvented, or even recreated to something new by us, which implies the protection by the copyright law.
Taking respectively copying a single element or using it in accordance with our composition or architecture is not allowed without proper licensing. But both was done by other entities alone and in collaboration (e.g. FOSHS).

Assigning operating system (os) tasks or functions to agents, specifically implementing every single os function as one agent, sounds like microService-Oriented Architecture (mSOA), but is not os-level virtualization or containerization.

Assigning Distributed operating system (Dos) and Robotic operating system (Ros) tasks or functions to Peer-to-Peer (P2P) Multi-Agent System (MAS) (P2P MAS) architectures, specifically implementing agents and related platform services as

Natural Language Processing (NLP), Dialogue System (DS), and Multimodal User Interface (MUI) (voice, (Artificial Neural Network (ANN) based) motion capture (e.g. head, and eye or gaze tracking)), and VR, but no 3D VW or VE, VR, and so on as GUI

Besides this, 3D VW or VE, and VR are only used for simulation and animation, and later AR and MR are only used for multimedia application, but not for Multimodal Graphical User Interface (MGUI) of and operating system (os) or a middleware.

one can also see MMUI, NLP, speech act between agent and human, even name service

as said in the past also we pushed agent and middleware down the system stack, even down to Internet, even further with NLP, etc. by overall integrating Ontologic System Architecture (OSA)

In the next step this has also been pushed down in the system stack, but also conceptually in itself.

Evoos and OS viewed as so-called Global Brain has also these MMUI capabilities. The overall integration was already explained on the basis of the field of NLP and NLU, Virtual Object System (VOS), and name service

with OS even ontology was replaced by (hyper)graph theory and set theory respectively ontologics
all liquid, dynamic, reflective, adaptive, scalable, flexible, resilient, etc.
Evoos "Ebenso ist die Forderung nach einer Koevolution eines Betriebssystems und seiner Grundlagen (siehe Muster flexible Grundlagen) nicht ohne den Einsatz einer Metaschichtenarchitektur realisierbar.==Similarly, the requirement for a co-evolution of an operating system and its foundations (see Pattern flexible foundations) cannot be realized without the use of a meta-layer architecture."

By integrating all in one the Ontologic System Architecture (OSA) fills and bridges all gaps and closes all loopholes.

Because Intelligent Networking (IN) was reactive and FIPA is external and reactive, and this whole field and its subfields discussed above were totally new in the year 1999, everything from user space or application layer, and middleware layer, specifically everything related to

• deliberative agent architecture, such as BDI agent architecture, and
• hybrid agent architecture,

and which has the known artistical, technological, and legal consequences related to our fields of

down into the kernel space or operating system layer, and artistical, technological, legal implications related to the fields of

• High-Throughput Computing (HTC),
• High Performance Computing (HPC),
• High Performance Communications (HPC or HPCom),
• High Productivity Computing (HPC or HProC),
• Parallel Computing (PC or ParaC)
• Cluster Computing (CC or ClusterC),
• Distributed SuperComputing (DSC or DSupC), including
  • Grid Computing (GC), and
  • Wide Area Network (WAN) SuperComputing (WANSC) or Interconnected SuperComputing (ISC),

are Evoos.

An architecture can be defined by its Application Programming Interface (API), but does not have to be defined in this way.

merely different segmentation, separation, and rearrangement of basic functions, modules or components, but same expression of idea
for this one can take only prior art, but obviously its range or scope in relation to Evoos and OS is quite limited

The differences between ABS, MAS (reactive FIPA, deliberative BDI, hybrid, real-time, simulation, VR, etc.), and Evoos show a wider gap, which does not suggest a seamless technological development respectively an ordinary technological progress, but implies a creation of a new work of art.

As in the field of cartography, which is the study and practice of making and using maps, small variations are sufficient to become eligible for copyright protection in the field of operating system. Of course, taking our Evoos and OS, and making again a small variation, but still taking our original and unique expression of idea as source of inspiration and blueprint, specifically the overall integrating Ontologic System Architecture (OSA), and HardBionic (HB) and SoftBionic (SB) properties, as seen with for example the partial OS variants Microsoft Windows, Google, Samsung, and Co. Android, Linux Foundation Linux, Apple iOS, and so on, is a copyright infringement.

Are there any questions?
At least everybody does know now what they get and what they pay for.

14.April.2022

Ontonics Further steps

We worked on our not so new electric motor series, including the Active Motor™ of our business unit Style of Speed™ (SoS™), which might became the third or fourth Game Changer™ Killer Product™ of our Ontonics™ Blitz Fund™ I Superbolt™ #4 Electric Power (EP).

Style of Speed Further steps

Over the last days we made some decisions about some last details of our special spare time and fun projects 9x9 and 91x respectively 91E project (see also for example the Style of Speed Further steps of the 7th of February 2022) based on our original and unique Active Differential™ #2 - The SuperArchitecture™ with Electric Torque Vectoring™ and 4 electric Active Actuators™ respectively Active Motors™, one motor for each wheel, Active Clang™ and other truly visionary and revolutionary solutions created and invented by C.S..

By the way:

15.April.2022

Style of Speed Further steps

We continued with the development of our System Truck™ for interested members, and artwork and technology licensing partners of our Society for Ontological Performance and Reproduction (SOPR), which in fact is a special System Automobile™, which allows us to simply construct Ontoscopes™ on Wheels as Purely Electric&153;, autonomous, connected, smart trucks without reinventing anything.

The following image shows a variant of a mobile hospital based on the original and unique inspiration of the Steinwinter SuperCargo 20.40, which was developed in the early years of the 1980s and we also had in mind when developing our original and unique solution.

©© 0 1.0 Rob Croes / Anefo
PD
Nationaal Archief

The SuperCargo increases the continuous loading area by 20 to 50% depending on the compared trucks and can be loaded at both ends and sides.
Even better, a further development allows to connect more than one prime mover and semitrailer to a road train.

The reason why a cab under still exists as an option is quite simple: In this way, a

16.April.2022

Style of Speed Further steps

We noticed that Electric Sports Cars™, specifically electric race cars, are extremely annoyingly and unacceptably loud screaming. Therefore, we developed a new gearbox, which is very silent.
But we are not sure if we need it with our Active Motors™ at all.

10:40 and 11:05 UTC+2
Investigations::Car or
Clarification ***

Porsche SE/Volkswagen→Porsche and JP Performance
What is wrong again? This is the question. We already had our Pure Electric™ vehicle RE copied as e-tron series followed by our Purely Electric™ vehicle Pan copied as Taycan, which Porsche wants to repeat with our Electric Sports Car™ (ESC) 9EE RSR and for sure our 9x9 and 91x respectively 91E project as something most potentially related to an electrified Cayman and for sure later to our electrified 911.
Indeed, it might be the next mission of them, but it is definitely not their vision once again, and we clearly said that it is not allowed to claim for our visions, creations, inventions, and so on.

We quote the talk in an advertisement video, which is about a concept car of the marque Porsche and was publicated on a video platform on the 7th of April 2022: "[...] - Wir fahren die Zukunft des Motorsports! - Porsche Mission [Style of Speed 9x9 and 91x]
[Jean Pierre Kraemer:] [...] Diese Video sollte eigentlich voll sein mit Dynamik, Spaß und Spannung wird es trotzdem [...].
[Technischer Projektleiter:] [...] Und [...] steht in 'ner lange Reihe mit vielen Jokers bei Porsche. Aber ist diesmal natürlich komplett rennsportorientiert. Und die Vision war zu zeigen wie 'n Kundenrennsportfahrzeug in 5 oder 6 Jahren aussehen könnte, das komplett elektrisch laufen würde. [...] Regularien [...] Und dann kam eben dieses Fahrzeug als [?]Statement, wo wie uns denken, dass ein Kundensportfahrzeug [...] aussehen könnte.
[Jean Pierre Kraemer:] [...]
[Technischer Projektleiter:] [...] rein elektrisch [...]
[Jean Pierre Kraemer:] [...] Das erste, was mir auffällt ist, der Radstand ist beeindrucken kurz, die Sitzposition ist beeindruckend tief. Das ist für mich eine Kombination, die bei einem Elektrofahrzeug auf den ersten Moment [reich]lich schwer umsetzbar klingt. Das heißt die Akkus sitzen [...]
[Technischer Projektleiter:] Also der Radstand ist [...] sieht kurz aus. Aber man läßt sich da gut täuschen. [...] wahnsinnig kurze &üuml;berhänge [...] Der Überhang vorne sind annähernd identisch mit [...] 982 Caymen zum Beispiel. Ist annähernd gleich. [...] Und der Radstand [...] ist 85 mm kürzer als 'n Caymen.
[Jean Pierre Kraemer:] [...]
[Technischer Projektleiter:] Also unser Zielgewicht sind 1500 [kg]. [...]
[Jean Pierre Kraemer:] Aber ist ja auch noch 'n Vorserien ... experimentierfahrzeug. [...] dass dieses Fahrzeug, was wir hier sehen, auch schon gewisse Anleihen zeigt von vielleicht einem Serienfahrzeug, was demnächst kommen könnte.
[Technischer Projektleiter:] [...] inzwischen patentierte Exoskelett auch ergeben [...]
[Jean Pierre Kraemer:] [...]
[Timo Bernhard:] [...]
[Jean Pierre Kraemer:] [...] Monocoque [...]?
[Technischer Projektleiter:] [...] Kundenrennsport baut in der Regel auf 'n bestehendes Fahrzeug auf, 'n Produktionsfahrzeug. Dass heißt von Anfang an war klar, das Fahrzeug wird kein Kohle-Monocoque bekommen. [...] Das ist 'n Stahlbau.
[Jean Pierre Kraemer:] [...] Stahlbau, deshalb auch patentiert mit dem Skelett da oben drauf. [...] Das heißt es gibt einen Verbindungspunkt, einen Klebepunkt, wo das Blech sich verbindet mit der Oberhaupt und steiff ist.
[Technischer Projektleiter:] [...] Hätte dann das Serienfahrzeug zum Beispield so 'ne Dach ohnehin, also Exoskelett, dann könnten wir das ohne Weiteres gleich für die FIA-Homologation nutzen und dann wäre der Käfig quasi schon eingebaut.
[Jean Pierre Kraemer:] Ich sehe aber auch Luft also wirklich Öffnungen, die gar nicht so klein sind. Das heißt, das hier ist Aero, die zum Heck, ... die durch strömt oder ist das 'ne Kühlleistung?
[Technischer Projektleiter:] Das ist Bremskühlung [...] die Hinterachse.
[Jean Pierre Kraemer:] [...] dass es von der Linie irgendwie Porsche ist, aber irgendwie auch gar nicht. Es wirkt irgendwie sehr neu und sehr frisch.
[Timo Bernhard:] Also mir gefällt am besten der Diffusor. [...]
[Technischer Projektleiter:] Es gab vorher 'mal 'n Grobkonzept von 2, 3 Leuten. Dann allerdings war das nur ein Maßkonzept, grob und was man vielleicht aussagen will und wo man hin will [...].
[Timo Bernhard:] [...] Dann kann man sich 'ne Eindruck machen [...] man kann das jetzt 'mal erleben, wo kann es hingehen in 5 oder 6 Jahren.
[Jean Pierre Kraemer:] [...] Thema Sound [...]
[Jean Pierre Kraemer:] [...] Das heißt er hat diese Taycan-Technologie mit einem 2-Ganggetriebe?
[Technischer Projektleiter:] Nein, 1-Ganggetriebe. Es geht wieder um die Erreichbarkeit für das Kundenteam. Also es wurde auch diskutiert 4 Motoren mit entsprechendem Torque-Vectoring und Handling-Dingen.
[Jean Pierre Kraemer:] Viel Entwicklungszeit [...]
[Technischer Projektleiter:] Genau und auch viel Applikationszeit an der Rennstrecke. [...]
[Jean Pierre Kraemer:] [...] Das Applizieren bedeutet, dass wenn man jetzt so was macht mit 4 einzelnen Motoren und ... man setzt nicht einfach 4 Motoren dran, lässt dann die Akkuleistung dran und kann einfach so Gas geben, sondern du willst ja einen gewissen Allradantrieb oder eine gewisse Gewohnheit, die der Mensch gewohnt ist von seinem Fahrverhalten ja auch applizieren auf 4 separat laufende Motoren und die sollen ja beim Herausbeschleunigen aus der Kurve ja früher, später ihr Drehmoment verteilen auf links ... auf alle vier, was es ja so nicht gibt, ne, du kannst ja nicht einzeln Leistung verteilen. Es gibt zwar 'n Torque-Vectoring, was dann halt was bremsen kann oder ein Rad, ja, bremsen kann, hochbeschleunigen separat, auch über eine Verteilung der Sperrwirkung, aber das ist mit 4 einzelnen Elektromotoren sehr, sehr kompliziert. Und ich denke mal 'ne Applikation für ein 4-Motorenfahrzeug sprechen wir von 10 Monaten?
[Technischer Projektleiter:] Na, es ist auch vor der Strecke. Wenn ich jetzt an der Strecke bin hab' andere Temperaturen, andere Reifengrößen, andere ..., dann muss ich an der Strecke neuapplizieren innerhalb von 'm gewissen Kennfeld und das wird natürlich ... Das wär' natürlich 'ne zusätzliche Hürde für diese Kundenteams ohne dass es sich irgendwo auszahlen würde.
[Jean Pierre Kraemer:] [...] Soundanimation. Wird Porsche dort vielleicht auch für zum Beispiel für ein Rennprojekt ein anderes ... dem Rennfahrer auf der Rennstrecke ein anderes Soundmodul geben. [...] geben wir auch dem motorsportbegeisterten Fahrer ... geben wir dem auch ein virtuelles Klangerlebnis in Zukunft?
[Timo Bernhard:] [...]
[Technischer Projektleiter:] Also das ist jetzt einfach noch zu früh d'rüber nachzudenken und es ist wirklich zu früh in der ganzen Entwicklung. Also bei dem Fahrzeug ist es eher so, dass die Getriebe so laut sind, dass man den Funk nicht versteht. Also da wird sich die Frage nach einem zusätzlichen Lautstärkesystem oder irgendwas vollkommen erübrigen.
[Timo Bernhard:] [...] Man hat mehr Möglichkeiten. Also Du hast es schon angesprochen Torque-Vectoring, also man kann ... oder mit Brake-by-Wire-System, man kann da schon die Balance später hinaus auch gut beeinflussen. Mit Torque-Vectoring, wo man 'n bisschen mehr Gear-Moment oder 'n bisschen mehr Rotation im Kurveneingang produziert haben. Also man hat da noch andere Möglichkeiten. [...] Und das ist genau der Punkt. [...]
[Technischer Projektleiter:] [...]
[Jean Pierre Kraemer:] [...] Die unteren Querlenker sind 'n 992 GT3 [...].
[Technischer Projektleiter:] [...] Die Achse unten, der Hilfsrahmen, der ist 'n modifizierter RSR-Rahmen. [...] Dieser Sitz beginnt vorne auf dem Armaturenbrett [...] und endet hinten auf der Hutablage. [...] Lenkrad [...] ist aus dem RSR übernommen, die Mechanik."]

Comment
We recall the description of our Active Differential™: "With two electric Active Motors™ and our intelligent Active Sensors™ at each driven axle, as well as the intelligent Active Control Management System, which centrally controls all Active Components and is powered by the High-Tech Operating System OntoLinux™, we have integrated the classical technologies of

in one system. The many ingenious features of our Active Differential™ are based on the fact that it replaced Gears by Software™ while keeping all advantages of and full compatibility to best automotive engineering, which is not realizable with concepts based on any kind of wheel hub motors or other all-electric drivetrain configurations."

Of course, our Active Differential™ #2 - The SuperArchitecture™ with Electric Torque Vectoring™

In fact, one can control acceleration and braking, and hence gear and rotation momenta in every moment, for example in the whole curve, at the entrance of the curve, in the curve, and at the exit of the curve.
Just forget the last 3 list points and keep in mind that we simply took a mobile robot with 2 or more wheels as foundational platform. Also look at mobile robot platforms with

and without steering system, which shows what one can do with such a control. Yes, one can rotate on the spot, though we would not recommend it due to the obviously huge abrasion.
The latter has already been shown to some extent by a plagiarism of our Active Differential™ #2 - The SuperArchitecture™ presented with a experimental electric powertrain of the company Bosch around the 7th of October 2010 (see the quoted report below and also for example the Investigations::Car #290 of the 2nd of October 2010).

We also quote a report, which is about an experimental electric automobile and was publicated on the 7th of October 2010: "Versuchsfahrzeug: Bosch baut Audi A5 zum E-Auto um
Einen serienmäßigen Audi A5 hat Bosch in nur sechs Monaten zum vollwertigen Elektromobil mit vielen technischen Raffinessen umgebaut. Das Antriebskonzept des Versuchsfahrzeugs soll nach Angaben des Autozulieferers die hohe Effizienz der Elektromobilität in Verbindung mit gesteigerter Agilität durch innovative Fahrdynamikfunktionen demonstrieren. Die Stuttgarter wollen sich damit als Entwicklungspartner der Autoindustrie für Hybrid- und E-Fahrzeuge empfehlen.
Die technischen Daten des Bosch-Audi können sich sehen lassen: Vier Elektromotoren mit jeweils 60 Kilowatt Leistung erzielen eine Gesamtleistung von 240 kW / 321 PS. An jedem individuell angetriebenen Rad steht zudem ein Drehmoment von 780 Nm zur Verfügung. Durch das so genannte "Torque Vectoring" kann jedes Rad einzeln beschleunigt und abgebremst werden. So lässt sich via Steuerelektronik jederzeit das Fahrverhalten spontan beeinflussen. Schiebt das Auto etwa in zu schnell angegangenen Kurven im Normal-Fahrmodus über die Kurvenräder nach außen, so neutralisiert der Rechner durch selektives Antreiben oder Abbremsen einzelner Räder diese Unart.
Ein weiteres Highlight stellt die Energie-Rückgewinnung beim Bremsen oder Gaswegnehmen (Rekuperation) dar. Das Modul hierzu stammt aus dem Bosch-Motorsport-Sektor und entspricht weitgehend der Formel-1-Technik namens KERS, bei der die Energie in einem Schwungrad gespeichert und auf Abruf wieder freigegeben wird.
Der E-Audi beschleunigt in nur sieben Sekunden auf Tempo 100 und ist in seiner jetzigen Form auf eine Höchstgeschwindigkeit von 130 km/h gedrosselt. Es seien aber umgekehrt deutlich über 200 km/h möglich, was aber zu Lasten der Reichweite ginge, so ein Konzernsprecher.

Serienproduktion ausgeschlossen
Zudem ist das Leergewicht des Konzeptfahrzeugs durch das Batteriegewicht (Li-Ionen-Bauweise, 380 Volt, 45 Kilowattstunden Kapazität) von einer halben Tonne auf schwergewichtige 1.900 Kilogramm gewachsen. Eine Serienproduktion, so Bosch, sei ausgeschlossen, da man nicht "in Konkurrenz zu einem bedeutenden Kunden" gehen wolle."

Comment
Obviously and doubtlessly, the

• followed exactly our description of our Active Differential™,
• demonstrated how it works in practice in general, and in curves in particular,
• showed that our technology is the true fun maker, and also tire slayer, tarmac destroyer, and mind blower, and
• even proved that "only minor changes are needed for every kind of automobile or truck to make it a Pure Electric™ vehicle. With our solution there are no barriers for acceptance and adoption".

But keep in mind, that it is not relevant and does not matter at all in relation to our Purely Electric™ Powertrain (PEP), Active Differential™, and Electric Torque Vectoring™, if such a powertrain is based on wheel hub motors or not, because the statement "which is not realizable with concepts based on any kind of wheel hub motors" refers to the

and the statement "which is not realizable with concepts based on any kind of [...] other all-electric drivetrain configurations" refers to the

but not to the general features of our ingeniuos idea, for which we still hold the moral rights and therefore proper referencing is always required as well as keeping our ArtWork (AW) 100% intact and unharmed due to the copyright law, competition law, and other laws being effective.

Furthermore, it has become obvious as well that Porsche and that video blogger do know the facts, including the existence of that experimental electric car of Audi and Bosch, because they emphasized the development time of that concept car of Porsche that lasted 9 months.

Last but not least, conducting an application at the race track is no problem at all in relation to the

17.April.2022

20:40 UTC+2
Style of Speed Further steps

We have worked on the design of our fspare time and un project 91E, which is based on our 91x GT (see also the Further steps of the 11th of January 2016, 14th of February 2017, and 14th of April 2022), but has a Pure Electric™ Powertrain (PEP), which is based on our Active Differential™ with Electric Torque Vectoring™, and much more.

The images below show some dirty doodles, which nevertheless are already able to transport and convey the expression of the evolved and maturing design.

© :I and Style of Speed

For sure, it is our signature Active Wing with swan neck.

We also worked on new design styles, which will underline the outer design and inner character of our oldtimers even more.

In this relation, we also would like to announce the

Next year. :)

We also continued the work on one of our not so new series of headlight and taillight, and we are very sure that our fans and readers will be quite fascinated and exited.

28.April.2022

Ontonics Further steps

We have new materials and parts made of them, and also suppliers, who have already acquired highly valuable competencies and skills. They will be amazed about future endeavours and potentially also become members of our enterprise.

We also looked at a relatively new production process.

22:08; 22:17, 23:30, 25:30 UTC+2
Style of Speed Further steps

In the last days, we completed the design of our special spare time and fun projects 9x9 and 91x respectively 91E project (see also for example the Further steps of the 7th of February 2022, 14th of April 2022, and 17th of April 2022 for the latest developments).
The result is beyond anything we have created before.

As we already said in the Further steps of the 17th of April 2022, we also made some experiments with new design styles of speed in relation to our models 9EE and 9EE RSR.

The following images should be able to transport our expression of idea, style, and stance.

© :I, :(, Rotiform, Wheels Boutique, and Style of Speed

The images will be improved.