智能体通过间接谈判与对手建模，实现高效博弈并提升收益。
Indirect Dynamic Negotiation in the Nash Demand Game
- 将自利博弈转化为贝叶斯学习与马尔可夫决策框架。
- 间接谈判使双方协作率提升，成功率最大化，个体收益增加。
- 适用于需要动态协商的多智能体场景，如经济博弈、资源分配。
本文研究了不完全信息下的顺序双边谈判问题。提出一种决策模型，使智能体通过间接谈判和学习对手模型来成功达成协议。方法上，将具有启发式动机的自利博弈纳入贝叶斯学习与马尔可夫决策过程框架。奖励函数的特殊形式隐式激励玩家通过闭环互动进行间接谈判。通过在纳什讨价还价博弈（Nash demand game）上的应用验证该方法：结果表明，所建立的谈判机制实现了：一）协调双方行为；二）最大化博弈成功率；三）为参与者带来更高个体收益。
原文摘要 · Abstract (English)
The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent's model. Methodologically the paper casts heuristically-motivated bargaining of a self-interested independent player into a framework of Bayesian learning and Markov decision processes. The special form of the reward implicitly motivates the players to negotiate indirectly, via closed-loop interaction. We illustrate the approach by applying our model to the Nash demand game, which is an abstract model of bargaining. The results indicate that the established negotiation: i) leads to coordinating players' actions; ii) results in maximising success rate of the game and iii) brings more individual profit to the players.
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