论文标题
关于多个问题的双边代理商谈判的可学习策略
Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues
论文作者
论文摘要
我们提出了一种新颖的双边谈判模型，该模型允许一个自私的代理商学习如何在存在用户偏好不确定性的情况下就多个问题进行谈判。该模型依赖于代表代理在谈判期间应采用的策略的可解释策略模板，并学习模板参数以最大程度地提高多次谈判中收到的平均效用，从而导致最佳的投标接受和生成。我们的模型还使用深度强化学习来评估需要它们的策略，从而为每个环境状态提供最佳的实用程序。为了处理用户喜好不确定性，该模型依靠随机搜索来找到最能与给定的部分优先配置文件一致的用户模型。在谈判时间内应用多目标优化和多准则决策方法，以产生帕累托最佳结果，从而增加成功（Win-Win）谈判的数量。严格的实验评估表明，采用我们模型的代理商在个人和社交福利公用事业方面优于第10自动谈判代理竞赛（ANAC'19）的获胜代理。
We present a novel bilateral negotiation model that allows a self-interested agent to learn how to negotiate over multiple issues in the presence of user preference uncertainty. The model relies upon interpretable strategy templates representing the tactics the agent should employ during the negotiation and learns template parameters to maximize the average utility received over multiple negotiations, thus resulting in optimal bid acceptance and generation. Our model also uses deep reinforcement learning to evaluate threshold utility values, for those tactics that require them, thereby deriving optimal utilities for every environment state. To handle user preference uncertainty, the model relies on a stochastic search to find user model that best agrees with a given partial preference profile. Multi-objective optimization and multi-criteria decision-making methods are applied at negotiation time to generate Pareto-optimal outcomes thereby increasing the number of successful (win-win) negotiations. Rigorous experimental evaluations show that the agent employing our model outperforms the winning agents of the 10th Automated Negotiating Agents Competition (ANAC'19) in terms of individual as well as social-welfare utilities.
