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

Toshiharu Sugawara*, Victor Lesser

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)129-153
Number of pages25
JournalMachine Learning
Issue number4
Publication statusPublished - 1998
Externally publishedYes


  • Coordination
  • Learning
  • Multiagent systems

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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