A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation

Abstract

Argument selection is considered the essence of the strategy in argumentation-based negotiation. An agent, which is arguing during a negotiation, must decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection policy. Following this policy, the agent observes some factors of the negotiation context, for instance: trust in the opponent and expected utility of the negotiated agreement, among others. Usually, argument selection policies are dened statically. However, as the negotiation context varies from a negotiation to another, dening a static selection policy it is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the dierent negotiation contexts as the agent´s experience increases. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection eciency by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment, and obtained promising results in both.