Agent learning in the multi-agent contracting system [MACS]
Decision Support Systems
A repeated Bayesian auction game for cognitive radio spectrum sharing scheme
Computer Communications
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Sim proposed a market-driven negotiation model [1] for designing negotiation agents. Although agent itself eagerness was taken into consideration as a fixed value in Simýs proposed model, opponent's eagerness was not considered. This paper proposes an improved market-driven negotiation model in which Bayesian updating rule is applied to learn opponent's eagerness since opponent's eagerness is unknown to an agent and may vary with dynamic changing market situation.