一个在谈判竞赛中获第二的智能代理，能动态识对手并调整策略。
ChargingBoul: A Competitive Negotiating Agent with Novel Opponent Modeling
- 通过分析对手出价模式分类对手，动态调整策略。
- 在2022年自动谈判竞赛中以微弱差距获个人效用第二。
- 适合研究自动化谈判、博弈策略的学者与工程师。
自动化谈判已成为多智能体系统中的关键研究领域，应用涵盖电子商务、资源分配和自主决策。本文提出ChargingBoul，一个参与2022年自动谈判代理竞赛（ANAC）并以极小差距获得个人效用第二名的谈判代理。ChargingBoul采用轻量但高效的战略，在让步与对手建模之间取得平衡，实现高谈判成效。该代理基于出价模式对对手进行分类，动态调整出价策略，并在谈判后期应用让步策略以最大化效用并促成协议。我们通过竞赛结果及后续研究中对代理的应用评估其表现。分析表明，ChargingBoul在多种对手策略下均具有效性，推动了自动化谈判技术的发展。同时讨论潜在改进方向，如更复杂的对手建模和自适应出价启发式方法，以进一步提升性能。
原文摘要 · Abstract (English)
Automated negotiation has emerged as a critical area of research in multiagent systems, with applications spanning e-commerce, resource allocation, and autonomous decision-making. This paper presents ChargingBoul, a negotiating agent that competed in the 2022 Automated Negotiating Agents Competition (ANAC) and placed second in individual utility by an exceptionally narrow margin. ChargingBoul employs a lightweight yet effective strategy that balances concession and opponent modeling to achieve high negotiation outcomes. The agent classifies opponents based on bid patterns, dynamically adjusts its bidding strategy, and applies a concession policy in later negotiation stages to maximize utility while fostering agreements. We evaluate ChargingBoul's performance using competition results and subsequent studies that have utilized the agent in negotiation research. Our analysis highlights ChargingBoul's effectiveness across diverse opponent strategies and its contributions to advancing automated negotiation techniques. We also discuss potential enhancements, including more sophisticated opponent modeling and adaptive bidding heuristics, to improve its performance further.
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