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.

翻译：自动谈判已成为多智能体系统研究中的一个关键领域，其应用涵盖电子商务、资源分配和自主决策。本文介绍了ChargingBoul，这是一种谈判智能体，参加了2022年自动谈判智能体竞赛（ANAC），并以极其微弱的差距在个体效用方面获得第二名。ChargingBoul采用了一种轻量级但有效的策略，通过平衡让步与对手建模来实现高谈判收益。该智能体根据出价模式对对手进行分类，动态调整其出价策略，并在谈判后期应用让步策略，以在促进协议达成的同时最大化效用。我们利用竞赛结果以及后续在谈判研究中应用该智能体的研究来评估ChargingBoul的性能。我们的分析突出了ChargingBoul在不同对手策略下的有效性及其对推进自动谈判技术的贡献。我们还讨论了潜在的改进方向，包括更复杂的对手建模和自适应出价启发式方法，以进一步提升其性能。