智能谈判代理通过动态策略优化，自适应调整报价并提升合作效率。
ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization
- 基于对手建模与以牙还牙原则，分三阶段优化每轮报价。
- 模拟与真人评估均显示其在动态博弈中表现更优。
- 可作为可解释的谈判教练，提供超越人类极限的建议。
谈判需在对话流中动态平衡自身利益与合作，以最大化自身收益。现有智能体因人类数据中的有限理性、对对手行为适应性差及战略推理能力弱而表现受限。为此，我们提出基于原则的谈判代理——ASTRA，一种基于对手建模与以牙还牙互惠原则的逐轮报价优化框架。ASTRA分三步运行：(1) 解读对手行为；(2) 通过集成线性规划（LP）求解器的工具化动作优化反报价；(3) 根据策略评估与对方接受概率选择最优报价。仿真与真人评估表明，该代理能有效适应对手立场变化，在增强适应性与战略推理下取得更优结果。除提升谈判性能外，还可作为强大教练工具，提供可解释的战略反馈与超人类理性的最优报价建议，其有效性经真人评价进一步验证。
原文摘要 · Abstract (English)
Negotiation requires dynamically balancing self-interest and cooperation within the flow of conversation to maximize one's own utility. Yet, existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. To address this, we introduce principle-driven negotiation agents, powered by ASTRA, a novel framework for turn-level offer optimization grounded in two core principles: opponent modeling and Tit-for-Tat reciprocity. ASTRA operates in three stages: (1) interpreting counterpart behavior, (2) optimizing counteroffers via a tool-integrated action with a linear programming (LP) solver, and (3) selecting offers based on strategy assessment and the partner's acceptance probability. Through simulations and human evaluations, our agent effectively adapts to an opponent's shifting stance and achieves favorable outcomes through enhanced adaptability and strategic reasoning. Beyond enhancing negotiation performance, it also serves as a powerful coaching tool, offering interpretable strategic feedback and optimal offer recommendations beyond human bounded rationality, with its potential further validated through human evaluation.
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