Negotiation requires dynamically balancing self-interest and cooperation 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 linear programming (LP) solver, and (3) selecting offers based on negotiation tactics 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 improving negotiation performance, it also serves as a powerful coaching tool, offering interpretable strategic feedback and optimal offer recommendations.

本研究针对现有谈判代理在理性有限性、适应性不足与战略推理能力有限方面的缺陷，提出了一种基于ASTRA的新框架。该框架通过对对手建模和以牙还牙的互惠原则，实现了基于行为解析和线性规划的动态提议优化，显著提升了谈判效果与代理的适应性，具有重要的应用潜力。

ASTRA：具有自适应和战略推理的动态提议优化谈判代理