Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fuzzy Game Theoretic Approach to Multi-Agent Coordination
PRIMA '98 Selected papers from the First Pacific Rim International Workshop on Multi-Agents, Multiagent Platforms
Evolutionary Equilibria Detection in Non-cooperative Games
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Evolutionary detection of new classes of equilibria: application in behavioral games
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Between selfishness and altruism: fuzzy nash--berge-zhukovskii equilibrium
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Standard game theory relies on the assumption that players are rational agents that try to maximize their payoff. Experiments with human players indicate that Nash equilibrium is seldom played. The goal of proposed approach is to explore more nuance equilibria by allowing a player to be biased towards different equilibria in a fuzzy manner. Several classes of equilibria (Nash, Pareto, Nash-Pareto) are defined by using appropriate generative relations. An evolutionary technique for detecting fuzzy equilibria is considered. Experimental results on Cournot’ duopoly game illustrate evolutionary detection of proposed fuzzy equilibria.