ISSN:
0885-6125
Keywords:
learning
;
multiagent
;
coordination
;
cooperative
;
hill-climbing
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract A central issue in the design of cooperative multiagent systems is how to coordinate the behavior of the agents to meet the goals of the designer. Traditionally, this had been accomplished by hand-coding the coordination strategies. However, this task is complex due to the interactions that can take place among agents. Recent work in the area has focused on how strategies can be learned. Yet, many of these systems suffer from convergence, complexity and performance problems. This paper presents a new approach for learning multiagent coordination strategies that addresses these issues. The effectiveness of the technique is demonstrated using a synthetic domain and the predator and prey pursuit problem.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1007562506751
