A learning based approach to modelling bilateral adaptive agent negotiations

Narayanan, Vidya (2008) A learning based approach to modelling bilateral adaptive agent negotiations. University of Southampton, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

In large multi-agent systems, individual agents often have conflicting goals, but are dependent on each other for the achievement of these objectives. In such situations, negotiation between the agents is a key means of resolving conflicts and reaching a compromise. Hence it is imperative to develop good automated negotiation techniques to enable effective interactions. However this problem is made harder by the fact that such environments are invariably dynamic (e.g. the bandwidth available for commu- nications can fluctuate, the availability of computation resources can change, and the time available for negotiations can change). Moreover, these changes can have a direct effect on the negotiation process. Thus an agent has to adapt its negotiation behaviour in response to changes in the environment and its opponent's behaviour if it is to be effective. Given this, this research has developed negotiation mechanisms that enable an agent to perform effectively in a particular class of negotiation encounters; namely, bilateral negotiation in which a service provider and a service consumer interact to fix the price of the service. In more detail, we use both reinforcement and Bayesian learning methods to derive an optimal agent strategy for bilateral negotiations in dynamic environments with incom- plete information. Specifically, an agent models the change in its opponent's behaviour using Markov Chains and determines an optimal policy to use in response to changes in the environment. Also using the Markov chain framework, the agent updates its prior knowledge of the opponent by observing successive offers using Bayesian inference

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