DocumentCode :
2882365
Title :
Dynamically Refining the Task Models of Agents in a Multi-Agent System: A Less Communication Intensive Approach
Author :
Wickramasinghe, L.K. ; Alahakoon, Damminda
Author_Institution :
Monash Univ., Clayton
fYear :
2006
fDate :
15-17 Dec. 2006
Firstpage :
177
Lastpage :
182
Abstract :
Action selection of agents in a given environment is governed by the utility they gained from the resulting environment state. In a multi-agent system (MAS), the autonomy of agents can lead to a situation for multiple agents to perform similar or identical tasks if they independently try to maximize self utilities. Therefore, it is important to dynamically identify the autonomous capabilities of the agents in the MAS and modify them to avoid any task overlapping. This paper presents a less communication intensive corporation and coordination strategy for refining the task models of agents on the fly using a collective reasoning process.
Keywords :
inference mechanisms; knowledge acquisition; multi-agent systems; statistical analysis; intensive corporation; knowledge extraction; multiagent system; reasoning process; statistical analysis; task model; Australia; Autonomous agents; Computer aided analysis; Information management; Information technology; Intelligent agent; Law; Legal factors; Multiagent systems; Routing; component; formatting; insert; style; styling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation, 2006. ICIA 2006. International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0555-6
Electronic_ISBN :
1-4244-0555-6
Type :
conf
DOI :
10.1109/ICINFA.2006.374106
Filename :
4250196
Link To Document :
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