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Bayesian Learning based Negotiation Agents for Supporting Negotiation with Incomplete Information

—An optimal negotiation agent should have capability for maximizing its utility even for negotiation with incomplete information in which the agents do not know opponent's private information such as reserve price (RP) and deadline. To support negotiation with incomplete negotiation, this work focuses on designing learning agents called Bayesian learning (BL) based negotiation agents (BLNA) adopting a time-dependent negotiation model. In BLNA, BL is used for estimating opponent's RP and the corresponding deadline is computed using the estimated RP. BLNA also has the capability of maximizing its utility using estimated deadline information when its deadline is shorter than or equal to the estimated deadline. To evaluate the performance of BLNA, BLNA is compared to an agent with complete information and an agent with incomplete information. Empirical results showed that BLNA achieved: 1) always higher utility than the agent with incomplete information for all cases, 2) close to or slightly lower utility than the agent with complete information when its deadline is lower and equal to opponent's deadline, and 3) lower utility than the agent with complete information when deadline is higher than opponent's deadline.