arXiv cs.AI· Eduardo de Avila-Armenta, Ra\'ul Alejandro Aquino-Ibarra, Jorge Octavio Nicol\'as-D\'iaz, Umberto Leon-Dominguez·· 1 小时前AI 评分44
AI 智能体替我们谈判之前：规则、搜索、优化、学习与 LLM 谈判引擎在稀缺条件下的竞争性锦标赛
Before AI Agents Negotiate for Us: A Competitive Tournament of Rule-Based, Search, Optimization, Learned, and Language-Model Negotiators Under Scarcity
AI 导读
一项 arXiv 研究将 9 种谈判架构（规则、搜索、优化、模仿学习、强化学习）同时放入 Gale-Shapley 双边匹配市场，在平衡与两种稀缺条件下轮转 36 对组合进行竞争。
正文 · 原文
Abstract:Automated bilateral negotiation underlies two-sided matching markets such as freight brokering, where many shippers and carriers negotiate price before a matching mechanism decides who is paired with whom. Prior strategy comparisons typically use isolated one-on-one duels or a single fixed market, so they cannot show how a strategy behaves against several live rivals under a changing balance of supply and demand. We built nine negotiation architectures spanning rule-based, search-based, optimization-based, imitation-learning, and reinforcement-learning families, and ran all nine simultaneously on both sides of a Gale-Shapley matching market under a balanced condition and two scarcity conditions rotated across all 36 exclusion pairs. In the balanced carrier market, an imitation-learned engine (ML2), a reinforcement-learning policy trained only on isolated negotiations (RL), and an expected-utility optimization engine (OPT) formed a statistically tied top group, and no architecture led in every condition and role. RL beat all eight carrier rivals when carriers were scarce, but its match rate collapsed from 62% to 16% when shippers were scarce and it had to compete with the other carrier agents for demand. A second reinforcement-learning policy trained against rival agents (RL2) kept its match rate above 91% in every condition, yet lost on realized payoff to all eight shipper-side rivals in two of the three conditions, traced to a confirmed train/deployment feature mismatch. We also tested nine large language models (LLMs), from 1 to 72 billion parameters, under the same protocol: they lost significantly to the two strongest engines (OPT2 and ML2) in 32 of 36 comparisons and won none. These results show that isolated or single-condition evaluation can hide failure modes that a many-sided, scarcity-varying, statistically corrected tournament exposes directly.
| Comments: | 28 pages, 10 figures, 7 tables. Code, data and trained models: this https URL (archived at doi:https://doi.org/10.5281/zenodo.24122038) |
| Subjects: | Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA) |
| ACM classes: | I.2.11; I.2.6 |
| Cite as: | arXiv:2611.15838 [cs.AI] |
| (or arXiv:2611.15838v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15838 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Eduardo De Avila Armenta [view email]
[v1]
Wed, 11 Nov 2026 18:48:20 UTC (431 KB)
来源：arXiv cs.AI · arxiv.org