@Article{zeng98a,
  author =	 {Dajun Zeng and Katia Sycara},
  title =	 {Bayesian learning in negotiation},
  journal =	 {International Journal of Human-Computer Studies},
  year =	 1998,
  volume =	 48,
  number =	 1,
  pages =	 {125--141},
  abstract =	 {Negotiation has been extensively discussed in
                  game-theoretic, economic and management science
                  literatures for decades. Recent growing interest in
                  autonomous interacting software agents and their
                  potential application in areas such as electronic
                  commerce has give 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. It provides an
                  adaptive, multi-issue negotiation model capable of
                  exhibiting a rich set of negotiation
                  behaviors. Within the proposed negotiation
                  framework, we model learning as a Bayesian belief
                  update process. In this paper, we present both
                  theoretical analysis and initial experimental
                  results showing that learning is beneficial in the
                  sequential negotiation model.},
  keywords =     {multiagent bayesian negotiation},
  url = 	 {http://jmvidal.cse.sc.edu/library/zeng98a.pdf},
  doi = 	 {10.1006/ijhc.1997.0164},
  googleid = 	 {iZDG20wKoLkJ:scholar.google.com/},
  cluster = 	 {13375702218511388809}
}