“你为什么不把这个任务分配给他们？”——协商感知的可解释任务分配与对比解释生成
`Why didn't you allocate this task to them?' Negotiation-Aware Explicable Task Allocation and Contrastive Explanation Generation
AI总结：
本文提出一种协商感知的集中式任务分配方法，通过模拟协商生成可解释分配，并利用反事实生成对比解释，实验证明其公平性和说服力，同时分析了信息不完整对解释长度的影响。
摘要：
任务分配是多智能体系统中的一个重要问题。当团队成员是人类，且对其队友的成本和整体性能指标了解不完善时，该问题变得更具挑战性。在本文中，我们提出了一种集中式人工智能任务分配（AITA）方法，该方法模拟协商过程，并生成协商感知的可解释任务分配。如果某个团队成员对提议的分配不满意，我们允许其使用反事实来质疑该分配。通过利用模拟协商的部分内容，我们能够提供对比解释，以最少的信息揭示他人的成本，从而反驳其反事实假设。通过人类研究，我们表明：（1）使用我们方法提出的分配在大多数人看来是公平的；（2）当提出反事实时，生成的解释易于理解且具有说服力。最后，我们通过实验研究了不同类型的信息不完整性对解释长度的影响，发现低估队友成本往往会增加解释长度。
英文摘要：
Task allocation is an important problem in multi-agent systems. It becomes more challenging when the team-members are humans with imperfect knowledge about their teammates' costs and the overall performance metric. In this paper, we propose a centralized Artificial Intelligence Task Allocation (AITA) that simulates a negotiation and produces a negotiation-aware explicable task allocation. If a team-member is unhappy with the proposed allocation, we allow them to question the proposed allocation using a counterfactual. By using parts of the simulated negotiation, we are able to provide contrastive explanations that provide minimum information about other's cost to refute their foil. With human studies, we show that (1) the allocation proposed using our method appears fair to the majority, and (2) when a counterfactual is raised, explanations generated are easy to comprehend and convincing. Finally, we empirically study the effect of different kinds of incompleteness on the explanation-length and find that underestimation of a teammate's costs often increases it.
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。
