BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information

Abstract

Abstract-Automated negotiation provides a means for resolving differences among interacting agents. For negotiation with complete information, this paper provides mathematical proofs to show that an agent's optimal strategy can be computed using its opponent's reserve price (RP) and deadline. The impetus of this work is using the synergy of Bayesian learning (BL)a n d genetic algorithm (GA) to determine an agent's optimal strategy in negotiation (N) with incomplete information. BLGAN adopts: 1) BL and a deadline-estimation process for estimating an opponent's RP and deadline and 2) GA for generating a proposal at each negotiation round. Learning the RP and deadline of an opponent enables the GA in BLGAN to reduce the size of its search space (SP) by adaptively focusing its search on a specific region in the space of all possible proposals. SP is dynamically defined as a region around an agent's proposal P at each negotiation round. P is generated using the agent's optimal strategy determined using its estimations of its opponent's RP and deadline. Hence, the GA in BLGAN is more likely to generate proposals that are closer to the proposal generated by the optimal strategy. Using GA to search around a proposal generated by its current strategy, an agent in BLGAN compensates for possible errors in estimating its opponent's RP and deadline. Empirical results show that agents adopting BLGAN reached agreements successfully, and achieved: 1) higher utilities and better combined negotiation outcomes (CNOs) than agents that only adopt GA to generate their proposals, 2) higher utilities than agents that adopt BL to learn only RP, and 3) higher utilities and better CNOs than agents that do not learn their opponents' RPs and deadlines.

An opponent model for agent-based shared decision-making via a genetic algorithm

“…Many studies use predictions of opponent preferences to accelerate the convergence of incomplete information negotiation and adopt various learning algorithms to improve the accuracy of opponent models, such as Bayesian algorithms (Sim et al, 2008 ; Pooyandeh and Marceau, 2014 ; Yi et al, 2021 ), neural networks (Zafari and Nassiri-Mofakham, 2016 ), and reinforcement learning (Bagga et al, 2021a ). Most of the research and applications of agent-based automatic negotiation models focus on linear values such as electronic market transactions and power transactions.…”

Two-stage mean-risk stochastic mixed integer optimization model for location-allocation problems under uncertain environment

“…In order to further evaluate the performance of our proposed MHB-PSO, we compare it with other discrete hybrid algorithms, such as binary PSO ( [12]) and GA ( [15,45]). The numerical results are shown in Table 3 and Figure 5.…”