Coevolving near-optimal strategies for negotiation with incomplete information using a diversity controlling GA

This work investigates the problem of finding near-optimal strategies for the bilateral negotiation with incomplete information between two competitive negotiation agents for resolving the differences in their objectives and preferences. While there are some former studies of finding negotiation strategies using evolutionary approaches, there are extremely few works on effectively locating the global optimal solution. In this study, we use a genetic algorithm (GA) to find near-optimal negotiation strategies for both negotiation agents conducting bilateral negotiation by coevolving both agents' strategies. Agents learn optimal strategies through trial-and-error, which is done by matching and negotiation of agents of one population with agents of the other population through random pairing in a one-to-one manner. However, a simple GA often cannot find the optimum results for both agents for the following reasons: (i) the premature convergence of a simple GA in itself, and (ii) both parties' failure to keep pace with each other's learning speed in the coevolution. To solve this problem, this paper proposes a GA which has a novel dynamic diversity controlling capability. The proposed method utilizes the accumulated frequency information of the occurrence of individuals in each band of the population for generations. The information is used in a diversification and refinement procedure of the proposed diversity controlling GA. Empirical results show that the proposed dynamic diversity controlling GA generally outperforms the traditional GA for finding near-optimal negotiation strategies.