首页   按字顺浏览 期刊浏览 卷期浏览 ACHIEVING FLEXIBLE AUTONOMY IN MULTIAGENT SYSTEMS USING CONSTRAINTS
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.

 

点击下载:  PDF (801KB)



返 回