Agent Communications Language

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Agent Communication Language (ACL), proposed by the Foundation for Intelligent Physical Agents (FIPA), is a proposed standard language for agent communications. Knowledge Query and Manipulation Language (KQML) is another proposed standard.

The most popular ACLs are:

Both rely on speech act theory developed by Searle in the 1960s [3] and enhanced by Winograd and Flores in the 1970s. They define a set of performatives, also called Communicative Acts, and their meaning (e.g. ask-one). The content of the performative is not standardized, but varies from system to system.

To make agents understand each other they have to not only speak the same language, but also have a common ontology. An ontology is a part of the agent's knowledge base that describes what kind of things an agent can deal with and how they are related to each other.

Examples of frameworks that implement a standard agent communication language (FIPA-ACL) include FIPA-OS [4] [5] and Jade. [6]

Related Research Articles

The Knowledge Query and Manipulation Language, or KQML, is a language and protocol for communication among software agents and knowledge-based systems. It was developed in the early 1990s as part of the DARPA knowledge Sharing Effort, which was aimed at developing techniques for building large-scale knowledge bases which are shareable and reusable. While originally conceived of as an interface to knowledge based systems, it was soon repurposed as an Agent communication language.

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

In the philosophy of language and linguistics, speech act is something expressed by an individual that not only presents information but performs an action as well. For example, the phrase "I would like the kimchi; could you please pass it to me?" is considered a speech act as it expresses the speaker's desire to acquire the kimchi, as well as presenting a request that someone pass the kimchi to them. According to Kent Bach, "almost any speech act is really the performance of several acts at once, distinguished by different aspects of the speaker's intention: there is the act of saying something, what one does in saying it, such as requesting or promising, and how one is trying to affect one's audience". The contemporary use of the term goes back to J. L. Austin's development of performative utterances and his theory of locutionary, illocutionary, and perlocutionary acts. Speech acts serve their function once they are said or communicated. These are commonly taken to include acts such as apologizing, promising, ordering, answering, requesting, complaining, warning, inviting, refusing, and congratulating.

In information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject.

John Florian Sowa is an American computer scientist, an expert in artificial intelligence and computer design, and the inventor of conceptual graphs.

<span class="mw-page-title-main">Symbolic artificial intelligence</span> Methods in artificial intelligence research

In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.

The Foundation for Intelligent Physical Agents (FIPA) is a body for developing and setting computer software standards for heterogeneous and interacting agents and agent-based systems.

In computer science, a software agent or software AI is a computer program that acts for a user or other program in a relationship of agency, which derives from the Latin agere : an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. Some agents are colloquially known as bots, from robot. They may be embodied, as when execution is paired with a robot body, or as software such as a chatbot executing on a phone or other computing device. Software agents may be autonomous or work together with other agents or people. Software agents interacting with people may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form.

<span class="mw-page-title-main">Multi-agent system</span> Built of multiple interacting agents

A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.

The belief–desire–intention software model (BDI) is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans and executing those plans. A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer.

Knowledge Interchange Format (KIF) is a computer language designed to enable systems to share and re-use information from knowledge-based systems. KIF is similar to frame languages such as KL-One and LOOM but unlike such language its primary role is not intended as a framework for the expression or use of knowledge but rather for the interchange of knowledge between systems. The designers of KIF likened it to PostScript. PostScript was not designed primarily as a language to store and manipulate documents but rather as an interchange format for systems and devices to share documents. In the same way KIF is meant to facilitate sharing of knowledge across different systems that use different languages, formalisms, platforms, etc.

<span class="mw-page-title-main">Tim Finin</span> American computer scientist

Timothy Wilking Finin is the Willard and Lillian Hackerman Chair in Engineering and is a Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). His research has focused on the applications of artificial intelligence to problems in information systems and has included contributions to natural language processing, expert systems, the theory and applications of multiagent systems, the semantic web, and mobile computing.

Java Agent Development Framework, or JADE, is a software framework for the development of software agents, implemented in Java. JADE system supports coordination between several agents FIPA and provides a standard implementation of the communication language FIPA-ACL, which facilitates the communication between agents and allows the services detection of the system. JADE was originally developed by Telecom Italia and is distributed as free software.

Machine interpretation of documents and services in Semantic Web environment is primarily enabled by (a) the capability to mark documents, document segments and services with semantic tags and (b) the ability to establish contextual relations between the tags with a domain model, which is formally represented as ontology. Human beings use natural languages to communicate an abstract view of the world. Natural language constructs are symbolic representations of human experience and are close to the conceptual model that Semantic Web technologies deal with. Thus, natural language constructs have been naturally used to represent the ontology elements. This makes it convenient to apply Semantic Web technologies in the domain of textual information. In contrast, multimedia documents are perceptual recording of human experience. An attempt to use a conceptual model to interpret the perceptual records gets severely impaired by the semantic gap that exists between the perceptual media features and the conceptual world. Notably, the concepts have their roots in perceptual experience of human beings and the apparent disconnect between the conceptual and the perceptual world is rather artificial. The key to semantic processing of multimedia data lies in harmonizing the seemingly isolated conceptual and the perceptual worlds. Representation of the Domain knowledge needs to be extended to enable perceptual modeling, over and above conceptual modeling that is supported. The perceptual model of a domain primarily comprises observable media properties of the concepts. Such perceptual models are useful for semantic interpretation of media documents, just as the conceptual models help in the semantic interpretation of textual documents.

CALO was an artificial intelligence project that attempted to integrate numerous AI technologies into a cognitive assistant. CALO is an acronym for "Cognitive Assistant that Learns and Organizes". The name was inspired by the Latin word "Calo" which means "soldier's servant". The project started in May 2003 and ran for five years, ending in 2008.

JACK Intelligent Agents is a framework in Java for multi-agent system development. JACK Intelligent Agents was built by Agent Oriented Software Pty. Ltd. (AOS) and is a third generation agent platform building on the experiences of the Procedural Reasoning System (PRS) and Distributed Multi-Agent Reasoning System (dMARS). JACK is one of the few multi-agent systems that uses the BDI software model and provides its own Java-based plan language and graphical planning tools.

The Contract Net Protocol (CNP) is a task-sharing protocol in multi-agent systems, introduced in 1980 by Reid G. Smith. It is used to allocate tasks among autonomous agents. It is close to sealed auctions protocols. It mainly relies on the Subcontractor: a manager proposes a task to several agents. The latter make a proposal among which the manager chooses to allocate the task. This task can then be divided and subcontracted.

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Flora-2 is an open source semantic rule-based system for knowledge representation and reasoning. The language of the system is derived from F-logic, HiLog, and Transaction logic. Being based on F-logic and HiLog implies that object-oriented syntax and higher-order representation are the major features of the system. Flora-2 also supports a form of defeasible reasoning called Logic Programming with Defaults and Argumentation Theories (LPDA). Applications include intelligent agents, Semantic Web, knowledge-bases networking, ontology management, integration of information, security policy analysis, automated database normalization, and more.

MaSMT is a free, lightweight Multi-agent system development framework, design through the Java environment. The MaSMT3 framework provides three types of agents, namely ordinary agent and managing agent and root agent. The managing agent capable to handle set of ordinary agent and the root agent capable to handle set of manager agents. MaSMT3.0 includes few features than the previous versions. MaSMT 3.0 includes root agent to handle swam of agents, Environment handling features to dynamically store agent's ontology, and notice board has been introducing to see required messages and events. In addition to these main features, agent status monitor has been introducing to view transporting messages. Multi-agent technology is modern software palindrome that capable of handling the complexity of a software system and providing intelligent solutions through the power of agent communication. A framework is a useful tool to develop multi-agent system and it saves lot of programmer's time and provides standards for the agent development.

References

  1. Poslad, Stefan (2007). "Specifying Protocols for Multi-agent System Interaction". ACM Transactions on Autonomous and Adaptive Systems. 2 (4): 15–es. doi:10.1145/1293731.1293735. S2CID   9477595.
  2. Finin, Tim; Richard Fritzson, Don McKay and Robin McEntire (1994). KQML as an agent communication language. Proceedings of the third international conference on Information and knowledge management, CIKM '94. pp. 456–463.
  3. Searle, J.R. (1969). Speech Acts. Cambridge University Press, Cambridge, UK.
  4. Poslad, Stefan; Philip Buckle and Robert Hadingham (2000). The FIPA-OS agent platform: Open Source for Open Standards. Proceedings of 5th International Conference on the Practical Application Of Intelligent Agents And Multi-Agent Technology (PAAM). pp. 355–368.
  5. Poslad, S; Buckle P, Hadingham R.G (2001). "Open Source, Standards and Scaleable Agencies". Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems. Lecture Notes in Computer Science. Vol. 1887. pp.  296–303. doi:10.1007/3-540-47772-1_30. ISBN   978-3-540-42315-7.
  6. Bellifeminee, Fabio; Agostino Poggi and Giovanni Rimassa (2001). JADE: a FIPA2000 compliant agent development environment. Proceedings of the fifth international conference on Autonomous agents. pp. 216–217.