  Scheduling is the task of assigning a set of scarce resources distributed
over time to a set of agents, who typically have preferences about the
assignments they would like to get. Due to the constrained nature of these
problems, satisfying all agents' preferences is often infeasible, which might
lead to some agents not being happy with the resulting schedule. Providing
explanations has been shown to increase satisfaction and trust in solutions
produced by AI tools. However, it is particularly challenging to explain
solutions that are influenced by and impact on multiple agents. In this paper
we introduce the EXPRES framework, which can explain why a given preference was
unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i)
an explanation generator that, based on a Mixed-Integer Linear Programming
model, finds the best set of reasons that can explain an unsatisfied
preference; and (ii) an explanation parser, which translates the generated
explanations into human interpretable ones. Through simulations, we show that
the explanation generator can efficiently scale to large instances. Finally,
through a set of user studies within J.P. Morgan, we show that employees
preferred the explanations generated by EXPRES over human-generated ones when
considering workforce scheduling scenarios.

Artificial Intelligence