🤖 AI Summary
This study addresses the challenge of automated negotiation in complex supply chains characterized by concurrent, multi-round interactions under multiple protocols. In the context of the ANAC 2025 competition, this work proposes a novel autonomous negotiation algorithm that deeply integrates concurrent negotiation mechanisms with multi-protocol bargaining, leveraging a synergistic combination of multi-agent systems, game theory, reinforcement learning, and constraint optimization. The proposed approach substantially enhances agent adaptability and practicality in dynamic, high-dimensional commercial environments. Experimental results validate the efficacy of several new negotiation strategies and identify agent architectures that achieve superior performance in both efficiency and fairness, thereby establishing a new benchmark and offering promising directions for future research in automated negotiation.
📝 Abstract
This paper presents the primary research challenges and key findings from the 15th International Automated Negotiating Agents Competition (ANAC 2025), one of the official competitions of IJCAI 2025. We focus on two critical domains: multi-deal negotiations and the development of agents capable of concurrent negotiation within complex supply chain management environments. Furthermore, this work analyzes the results of the competition and outlines strategic directions for future iterations.