多议题双边Agent协商的可学习策略
Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues
摘要：
我们提出了一种新的双边谈判模型，允许一个自私的代理学习如何在用户偏好的不确定性存在的多个问题进行谈判。该模型依赖于可解释的策略模板，代表代理在谈判过程中应该采用的策略，并学习模板参数，以最大限度地提高在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阈值效用值，用于那些需要它们的策略，从而为每个环境状态导出最佳效用。为了处理用户偏好的不确定性，该模型依赖于一个随机搜索，以找到最符合给定的部分偏好配置文件的用户模型。多目标优化和多标准决策方法应用于谈判时间，以产生帕累托最优结果，从而增加成功（双赢）谈判的数量。严格的实验评估表明，代理采用我们的模型优于获奖代理的第10届自动谈判代理竞争（ANAC'19）的个人以及社会福利效用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
