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- W1566156380 abstract "This paper addresses cross-validation in multiagent-based simulation by analyzing evolutionary agents in a bargaining game in game theory. In particular, this paper focuses on analyzing different learning mechanisms and knowledge representation capabilities applied to agents for cross-validation. To investgate hese issues, we compare the following two cases: (1) agents employing an evolutionary stratgy (ES) and agents employing a learnig classifier system (LCS) as different learning mechanisms; and (2) agents handling an ordinary explanation of numbers and agents handling a limited explanation of numbers as different knowlege representation capabilities. An intensive comparison of simulation results reveal the following implications: (1) simulation results b ES-based agents show the same tendency in game theory but those by LCS-based agents do not; and (2) even simulation results by ES-based agents become strange when the agents are restricted to handling only a real number with two decimal dgits instead of an ordinary real number in a negotiation process between the agents in the bagaining game." @default.
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- W1566156380 date "2002-07-09" @default.
- W1566156380 modified "2023-09-23" @default.
- W1566156380 title "Cross-validation In Multiagent-based Simulation: Analyzing Evolutionary Bargaining Agents" @default.
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