Learning to Negotiate Optimally in Non-Stationary Environments

Abstract. 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.