ABiNeS: An Adaptive Bilateral Negotiating Strategy over Multiple Items

Abstract

Multi-item negotiations surround our daily life and usually involve two parties that share common or conflicting interests. Effective automated negotiation techniques should enable the agents to adaptively adjust their behaviors depending on the characteristics of their negotiating partners and negotiation scenarios. This is complicated by the fact that the negotiation agents are usually unwilling to reveal their information (strategies and preferences) to avoid being exploited during negotiation. In this paper, we propose an adaptive negotiation strategy, called ABiNeS, which can make effective negotiations against different types of negotiating partners. The ABiNeS agent employs the non-exploitation point to adaptively adjust the appropriate time to stop exploiting the negotiating partner and also predicts the optimal offer for the negotiating partner based on reinforcement-learning based approach. Simulation results show that the ABiNeS agent can perform more efficient exploitations against different negotiating partners, and thus achieve higher overall utilities compared with the state-of-the-art negotiation strategies in different negotiation scenarios.