Dajun Zeng, Katia Sycara
Negotiation has been extensively discussed in game-theoretic, economic, and management science literatures for decades. Recent growing interest in electronic commerce has given increased importance to automated negotiation. Evidence both from theoretical analysis and from observations of human interactions suggests that if decision makers can somehow take into consideration what other agents are thinking and furthermore learn during their interactions how other agents behave, their payoff might increase. In this paper, we propose a sequential decision making model of negotiation, called Bazaar. Within the proposed negotiation framework, we model learning as a Bayesian belief update process. In this paper, we explore the hypothesis that learning is beneficial in sequential negotiation and present initial experimental results.