'Why didn't you allocate this task to them?' Negotiation-Aware Explicable Task Allocation and Contrastive Explanation Generation

In this work, we design an Artificially Intelligent Task Allocator (AITA) that proposes a task allocation for multi-agent systems especially with humans. A key property of this allocation is that when an agent with imperfect knowledge (about their teammate's costs and/or the team's performance metric) questions the allocation by contesting with a counterfactual, a contrastive explanation is provided to answer their challenge. For this, we consider a negotiation process that produces a negotiation-aware task allocation and, in turn, leverages a negotiation tree to provide a contrastive explanation. With human subject studies, we show that the proposed allocation indeed appears fair to a majority of participants, and the explanations generated are easy to comprehend and convincing.