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A Negatiation platform for cooperating multi-agents systems

Authors: Polat, Faruk;

A Negatiation platform for cooperating multi-agents systems

Abstract

Research in Distributed Artificial Intelligence attempts to integrate and coordinate the activities of multiple, intelligent problem solvers that interact to solve complex tasks in domains such as design, medical diagnosis, business management, and so on. Due to the different goals, knowledge and viewpoints of the agents, conflicts might arise at any phase of the problem-solving process. Managing diverse knowledge requires well-organized models of conflict resolution. In this thesis, a computational model for cooperating intelligent agents which openly supports multi-agent conflict detection and resolution is described. The model is based on the insights that each agent has its own conflict management knowledge which is separated from its domain level knowledge. Each agent has its own conflict management knowledge which is not accessible or visible to others. Furthermore, there are no globally known conflict resolution strategies. Each agent involved in a conflict chooses a resolution scheme according to its self-interest. The problem-solving environment allows a new problem solver to be added or an existing one to be removed, without requiring any modification of the rest of the model, and therefore achieves open information system semantics.

Includes bibliographical references (leaves 96-107).

Cataloged from PDF version of article.

by Faruk Polat

Country
Turkey
Related Organizations
Keywords

Artificial intelligence, Conflict detection, Q337 .P65 1993, Distributed artificial intelligence, Conflict management knowledge, Conflict management, Conflict resolution, Computer Engineering and Computer Science and Control, Intelligent tutoring systems, Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green