On Learning Negotiation Strategies by Artificial Adaptive Agents in Environments of Incomplete Information

Abstract

Automated negotiation by artificial adaptive agents (AAAs) holds great promise for electronic commerce, but non-trivial, practical issues remain. Published studies of AAA learning of negotiation strategies have been based on artificial environments that include complete payoff information for both sides of the bargaining table. This is not realistic in applied contexts. Without loss of generality, we consider the case of a seller who knows its own preferences over negotiation outcomes but will have limited information about the private values of each customer. We propose a learning environment that takes advantage of partial information likely to be available to the vendor. General strategies are learned for a group of similar customers - a market segment - through a simulation approach and a genetic learning algorithm. In addition, we systematically further relax constraints on the opponent’s preferences to further explore AAA learning in incomplete information environments.