An opponent-adaptive strategy to increase utility and fairness in agents’ negotiation

摘要

In automatic negotiation, intelligent agents try to reach the best deal possible on behalf of their owners. In previous studies, opponent modeling of a negotiator agent has been used to tune the final bid out of a group of bids chosen by the agent’s strategy. In this research, a time-based bidding strategy has been introduced, which uses the opponent model to concede more adaptively to the opponents, thereby achieving an improved utility, social welfare, and fairness for the agent. By modeling the preference profile of the opponent during the negotiation session, this strategy sets its concession factor proportional to the model. Experiments show that in comparison to state-of-the-art agents, this agent makes better agreements in terms of individual utility and social welfare in small and medium-sized domains and can, in some cases, increase the performance up to 10%. The proposed agent successfully gets the deal up to 37% closer to best social bids in terms of distance to the Pareto frontier and the Nash point. An implementation based on the proposed strategy was used in an agent called AgreeableAgent, which participated in the international ANAC 2018 and won first place in individual utility rankings.