Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments

Toshiharu Sugawara, Victor Lesser

研究成果: Article査読

34 被引用数 (Scopus)

抄録

Coordination is an essential technique in cooperative, distributed multiagent systems. However, sophisticated coordination strategies are not always cost-effective in all problem-solving situations. This paper presents a learning method to identify what information will improve coordination in specific problem-solving situations. Learning is accomplished by recording and analyzing traces of inferences after problem solving. The analysis identifies situations where inappropriate coordination strategies caused redundant activities, or the lack of timely execution of important activities, thus degrading system performance. To remedy this problem, situation-specific control rules are created which acquire additional nonlocal information about activities in the agent networks and then select another plan or another scheduling strategy. Examples from a real distributed problem-solving application involving diagnosis of a local area network are described.

本文言語English
ページ(範囲)129-153
ページ数25
ジャーナルMachine Learning
12
4
DOI
出版ステータスPublished - 1998
外部発表はい

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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