Adaptive bargaining agents that negotiate optimally and rapidly

Whereas many extant works only adopt utility as the performance measure for evaluating negotiation agents, this work formulates strategies that optimize combined negotiation outcomes in terms of utilities, success rates, and negotiation speed. In some applications (e.g., Grid resource management), negotiation agents should be designed such that they are more likely to acquire resources more rapidly and with more certainty (in addition to optimizing utility). For negotiations with complete information, mathematical proofs show that the negotiation strategy set in this work optimizes the utilities of agents while guaranteeing that agreements are reached. A novel algorithm BLGAN is devised to guide agents in negotiations with incomplete information. BLGAN adopts 1) a Bayesian learning (BL) approach for estimating the reserve price of an agent's opponent, and 2) a multi-objective genetic algorithm (GA) for generating a proposal at each negotiation (N) round. In bilateral negotiations with incomplete information, empirical results show that when both agents adopt BLGAN to learn each other's reserve price, they are both guaranteed to reach agreements, and complete negotiations with much fewer negotiation rounds. When only one agent adopts BLGAN, the agent was highly successful in reaching agreements, achieved average utilities that were much closer to optimal, and used fewer negotiation rounds than the agent that did not adopt BLGAN.