  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.
