Contrastive Explanations of Centralized Multi-agent Optimization Solutions

Parisa Zehtabi, Alberto Pozanco, Ayala Bloch, Daniel Borrajo, Sarit Kraus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 34th International Conference on Automated Planning and Scheduling, ICAPS 2024
EditorsSara Bernardini, Christian Muise
PublisherAssociation for the Advancement of Artificial Intelligence
Pages671-679
Number of pages9
ISBN (Electronic)9781577358893
DOIs
StatePublished - 30 May 2024
Event34th International Conference on Automated Planning and Scheduling, ICAPS 2024 - Banaff, Canada
Duration: 1 Jun 20246 Jun 2024

Publication series

NameProceedings International Conference on Automated Planning and Scheduling, ICAPS
Volume34
ISSN (Print)2334-0835
ISSN (Electronic)2334-0843

Conference

Conference34th International Conference on Automated Planning and Scheduling, ICAPS 2024
Country/TerritoryCanada
CityBanaff
Period1/06/246/06/24

Bibliographical note

Publisher Copyright:
Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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