ACHIEVING FLEXIBLE AUTONOMY IN MULTIAGENT SYSTEMS USING CONSTRAINTS
作者:
MARK EVANS,
JOHN ANDERSON,
GEOFF CRYSDALE,
期刊:
Applied Artificial Intelligence
(Taylor Available online 1992)
卷期:
Volume 6,
issue 1
页码: 103-126
ISSN:0883-9514
年代: 1992
DOI:10.1080/08839519208949944
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Organizations influence many aspects of our lives. They exist for one reason: they can accomplish things that individuals cannot. While recent work in high-autonomy systems has shown that autonomy is a critical issue in artificial intelligence (AI) systems, these systems must also be able to cooperate with and rely on one another to deal with complex problems. The autonomy of such systems must be flexible, in order that agents may solve problems on their own as well as in groups. We have developed a model of distributed problem solving in which coordination of problem-solving agents is viewed as a multiagent constraint-satisfaction planning problem. This paper describes the experimental testbed that we are currently developing to facilitate the investigation of various constraint-based strategies for addressing the coordination issues inherent in cooperative distributed problem-solving domains.
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