Learning message-related coordination control in multiagent systems

Toshiharu Sugawara, Satoshi Kurihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


This paper introduces the learning mechanism by which agents can identify, through experience, important messages in the context of inference in a specific situation. At first, agents may not be able to immediately read and process important messages because of inappropriate ratings, incomplete non-local information, or insufficient knowledge for coordinated actions. By analyzing the history of past inferences with other agents, however, they can identify which messages were really used. Agents then generate situation-specific rules for understanding important messages when a similar problem-solving context appears. This paper also gives an example for explaining how agents can generate the control rule.

Original languageEnglish
Title of host publicationMulti-Agent Systems
Subtitle of host publicationTheories, Languages, and Applications - 4th Australian Workshop on Distributed Artificial Intelligence, 1998, Selected Papers
EditorsChenqi Zhang, Dickson Lukose
PublisherSpringer Verlag
Number of pages16
ISBN (Print)3540654771, 9783540654773
Publication statusPublished - 1998
Externally publishedYes
Event4th Australian Workshop on Distributed Artificial Intelligence, DAK 1998 - Brisbane, Australia
Duration: 1998 Jul 131998 Jul 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other4th Australian Workshop on Distributed Artificial Intelligence, DAK 1998


  • Multi-agent learning
  • Multi-agent planning
  • Reasoning about coordinated interactions

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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