论文标题
“你为什么不将此任务分配给他们？”谈判意识到的明确任务分配和对比解释产生
`Why didn't you allocate this task to them?' Negotiation-Aware Explicable Task Allocation and Contrastive Explanation Generation
论文作者
论文摘要
任务分配是多代理系统中的重要问题。当团队成员是人类对队友的成本和整体绩效指标的知识不完全的知识时，这变得更加具有挑战性。在本文中，我们提出了一个集中的人工智能任务分配（AITA），该任务模拟了谈判并产生了谈判意识到的明确任务分配。如果团队成员对提议的分配不满意，我们允许他们使用反事实来质疑提议的分配。通过使用模拟谈判的一部分，我们能够提供对比解释，以提供有关他人反驳其箔纸的最低信息。通过人类研究，我们表明（1）使用我们的方法提出的分配对大多数人来说似乎是公平的，并且（2）当提出反事实时，产生的解释易于理解和令人信服。最后，我们从经验上研究了不同种类的不完整性对解释长度的影响，发现小组成本的低估通常会增加。
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.
