Learning to Negotiate Optimally in Non-stationary Environments

  • Vidya Narayanan
  • Nicholas R. Jennings
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4149)


We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal strategy in incomplete information settings. Specifically, an agent learns the optimal strategy to play against an opponent whose strategy varies with time, assuming no prior information about its negotiation parameters. In so doing, we present a new framework for adaptive negotiation in such non-stationary environments and develop a novel learning algorithm, which is guaranteed to converge, that an agent can use to negotiate optimally over time. We have implemented our algorithm and shown that it converges quickly in a wide range of cases.


Multiagent System Negotiation Process Bayesian Learning Learning Agent Automate Negotiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vidya Narayanan
    • 1
  • Nicholas R. Jennings
    • 1
  1. 1.Intelligence, Agents, Multimedia, School of Electronics and Computer ScienceUniversity of SouthamptonUK

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