Contrastive Explanations of Centralized Multi-agent Optimization Solutions


  • Parisa Zehtabi J.P. Morgan AI Research
  • Alberto Pozanco J.P. Morgan AI Research
  • Ayala Bolch Ariel University
  • Daniel Borrajo J.P. Morgan AI Research
  • Sarit Kraus Department of Computer Science, Bar-Ilan University



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.




How to Cite

Zehtabi, P., Pozanco, A., Bolch, A., Borrajo, D., & Kraus, S. (2024). Contrastive Explanations of Centralized Multi-agent Optimization Solutions. Proceedings of the International Conference on Automated Planning and Scheduling, 34(1), 671-679.