Contrastive Explanations of Centralized Multi-agent Optimization Solutions
Proceedings of the International Conference on Automated Planning and Scheduling(2023)
Abstract
In many real-world scenarios, agents are involved in optimization problems.
Since most of these scenarios are over-constrained, optimal solutions do not
always satisfy all agents. Some agents might be unhappy and ask questions of
the form “Why does solution S not satisfy property P?”. We propose CMAoE,
a domain-independent approach to obtain contrastive explanations by: (i)
generating a new solution S^' where property P is enforced, while also
minimizing the differences between S and S^'; and (ii) highlighting
the differences between the two solutions, with respect to the features of the
objective function of the multi-agent system. Such explanations aim to help
agents understanding why the initial solution is better in the context of the
multi-agent system than what they expected. We have carried out a computational
evaluation that shows that CMAoE can generate contrastive explanations for
large multi-agent optimization problems. We have also performed an extensive
user study in four different domains that shows that: (i) after being presented
with these explanations, humans' satisfaction with the original solution
increases; and (ii) the constrastive explanations generated by CMAoE are
preferred or equally preferred by humans over the ones generated by state of
the art approaches.
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