Indexed by:会议论文
Date of Publication:2012-06-09
Included Journals:EI、Scopus
Volume:7345 LNAI
Page Number:689-698
Abstract:Cooperation among agents is critical for agents' Artificial Intelligence (AI). In multi-agent system (MAS), agents cooperate with each other for long-term return and build such partnership in most of the time. However, the partnership could be broken easily if one agent did not or refused to grant a favor to another. Will it be helpful to MAS or individual agent, if agent has controllable level of tolerance? That is the main question of this paper. In order to find an answer, we propose a cooperative strategy, "flexible reciprocal altruism model (FRAM)". In FRAM, agent has a controllable rate of tolerance and is willing to grant favors for long-term return. Agent can determine whether to grant a favor to another based on their past interactions. As a result, granting unmatched favors by accident will not break the relationship between two agents immediately. Experiments show that our strategy performs well with different cost/value tradeoffs, numbers of agents, and load. ? 2012 Springer-Verlag.
Professor
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates
Gender:Male
Alma Mater:大连理工大学
Degree:Doctoral Degree
School/Department:系统工程研究所
Discipline:Management Science and Engineering. Systems Engineering
Business Address:经济管理学院D533
Contact Information:hxxia(at)dlut(dot)edu(dot)cn 电话:0411-84706689
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