A Multi-Agent Based Framework for Supporting Learning in Adaptive Automated Negotiation

We propose a multi-agent based framework for supporting adaptive bilateral automated negotiation during buyer-seller agent interactions. In this work, these interactions are viewed as a cooperative game (from the idea of two-person game theory, nonzerosum game), where the players try to reach an agreement about a certain negotiation object that is offered by one player to another. The final agreement is assumed to be satisfactory to both parts. To achieve effectively this goal, we modelled each player as a multi-agent system with its respective environment. In doing so, we aim at providing an effective means to collect relevant information to help agents to make good decisions, that is, how to choose the “best way to play” among a set of alternatives. Then we define a mechanism to model the opponent player and other mechanisms for monitoring relevant variables from the player ́ environment. Also, we maintain the context of the current game and keep the most relevant information of previous games. Additionally, we integrate all the information to be used in the refinement of the game strategies governing the multi-agent system.