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Distributed artificial intelligence

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Distributed artificial intelligence (DAI) is a subfield of artificial intelligence research dedicated to the development of distributed solutions for complex problems regarded as requiring intelligence. DAI is closely related to and a predecessor of the field of Multi-Agent Systems.

Examples

Many examples and descriptions exists for an Distributed artificial intelligence but essentially it is a knowledge management and knowledge production tool capable of providing a unified working method among firms, research centers, institutions, universities, think tanks, consultants, and other organizations. For comparison, an example of Distributed Intelligence is described by PC Magazine as a means of dynamic knowledge creation. An example of Distributed artificial intelligence is found in the capability of creating Resource Description Frameworks as defined by the W3C Organization for the advancement of the Semantic Web. Another example of Distributed artificial intelligence has the characteristics defined as an Innovative System which is one of four requirements by the World Bank Institute for a country to participate in the knowledge economy.

Concepts

The concept of Distributed Artificial Intelligence is defined as being capable of knowledge representation by disseminating tags using Ontologies. The ‘’Cognitive Concepts‘’ are formed using Ontologies that are Axiologically Valued in the process of dynamically creating a Resource Description Framework (RDF). An RDF is disseminated from a collection of data defined as a knowledgebase. [1] The concept of a knowledgebase disseminated RDF would follow standards such as XML file formatting to meet universal portability. Such portability allows for the desired RDF to be downloadable as an attachment to that knowledgebase and may be used with any compatible Agent or its own intelligent agent. One example is the dissemination and attachment of an RDF as part of an online encyclopedia which would transform it into a knowledgebase capable of connecting a host of Intelligent Agents using Ontology Inference. An end-user PC operating such an Intelligent Agent or sometimes referred to as a ‘’Software Agent’’ may select any knowledgebase file and download the RDF into the Agent. Proceeding, the concept is once the RDF is animated by the Intelligent Agent, the end-users content is given semantic interoperability by Axiological Valuation relative to the contextualization of the selected knowledgebase creating an ODIS (Ontology-based Domain Repository).[2] Distributed Intelligence occurs when a group of Intelligent Agents have a directive that is syntactically poised in a logical question to resolve problems using contextualization as the result for knowledge creation. Therefore, through Axiological Valuations of the Resource Description Framework’s Ontology allows a systems approach to such innovations as the ability to create knowledge in the form of a commodity.

Objective

There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstreams in DAI research include the following:

  • Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that multiprocessor systems and clusters of computers can be used to speed up calculation.
  • Distributed problem solving (DPS): the concept of agent, autonomous entities that can communicate with each other, was developed to serve as an abstraction for developing DPS systems. See below for further details.
  • Multi-Agent Based Simulation (MABS): a branch of DAI that builds the foundation for simulations that need to analyze not only phenomena at macro level but also at micro level, as it is in many social simulation scenarios.

Software agents

The key concept used in DPS and MABS is the abstraction called software agents. An agent is a virtual (or physical) autonomous entity that has an understanding of its environment and acts upon it. An agent is usually able to communicate with other agents in the same system to achieve a common goal, that one agent alone could not achieve. This communicate system uses an agent communication language.

A first classification that is useful is to divide agents into:

  • reactive agent – A reactive agent is not much more than an automaton that receives input, processes it and produces an output.
  • deliberative agent – A deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans.
  • hybrid agent – A hybrid agent is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation.

Well-recognized agent architectures that describe how an agent is internally structured are:

  • Soar (a rule-based approach)
  • BDI (Believe Desire Intention, a general architecture that describes how plans are made)
  • InterRAP (A three-layer architecture, with a reactive, a deliberative and a social layer)
  • PECS (Physics, Emotion, Cognition, Social, describes how those four parts influences the agents behavior).

See also

References

  1. ^ Davis, Miles. “Industry Roadmap to Web 3.0 & Multibillion Dollar Market Opportunities”, Project10X Semantic Wave Report, 2009, pg 8
  2. ^ Michael Sintek, Stefan Decker: TRIPLE - A Query, Inference, and Transformation Language for the Semantic Web., First International Semantic Web Conference 2002: 364-378.

Further reading

  • Hewitt, Carl; and Jeff Inman (November/December 1991). "DAI Betwixt and Between: From 'Intelligent Agents' to Open Systems Science" IEEE Transactions on Systems, Man, and Cybernetics. Volume: 21 Issue: 6, pps. 1409–1419. ISSN 0018-9472
  • Shoham, Yoav; Leyton-Brown, Kevin (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. New York: Cambridge University Press. ISBN 978-0-521-89943-7.
  • Sun, Ron, (2005). Cognition and Multi-Agent Interaction. New York: Cambridge University Press. ISBN 978-0521839648
  • Vlassis (2008). A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence. San Rafael, CA: Morgan & Claypool Publishers. ISBN 978-1-598-29526-9. {{cite book}}: |first2= missing |last2= (help)
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