An offer-generating strategy for multiple negotiations with mixed types of issues and issue interdependency

Agent negotiation in multi-agent systems has been extensively studied, focusing on both theoretical and applied research. However, a limited number of studies have considered proposing an offer-generating strategy for agents to propose offers during the negotiation process in the multiple-negotiation situation where interdependency exist between a mixture of discrete issues and continuous issues across different negotiations. Especially, considering the above common real-life situation, there is little work of proposing such a strategy which is able to generate an approximately Pareto optimal solution. To address such challenges, this paper targets at multiple-negotiation scenarios involving interdependency between mixed types of issues across different negotiations. The contributions of this paper are threefold. Firstly, this paper addresses the research gap in mixed-type of issues in multiple negotiations. Secondly, the paper introduces a formalized negotiation model for multiple-negotiation scenarios, addressing both discrete and continuous issues, enabling automatic agents to obtain goal-aligned offers effectively. Thirdly, this paper introduces a Hybrid of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) Algorithm (i.e., named as HPGA in this paper) as an offer-generating strategy to assist agents in achieving approximately Pareto optimization in multiple-negotiation scenarios. To support those claims, this paper presents an overall modeling framework, introduces the proposed offer-generation strategy, conducts a series of experiments to demonstrate the superiority of the proposed approach in this paper, and presents two realistic case studies. Overall, this research expands upon existing studies in agent-based negotiation by addressing the overlooked aspects of mixed types of issues and issue interdependency across multiple negotiations. The proposed modeling approach and offer-generation strategy contribute to the advancement of negotiation techniques in multi-agent systems.