The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model cooperative multi-agent problems that need to be solved distributively. A core assumption of existing approaches is that DCOP solutions can be easily understood, accepted, and adopted, which may not hold, as evidenced by the large body of literature on Explainable AI. In this paper, we propose the Explainable DCOP (X-DCOP) model, which extends a DCOP to include its solution and a contrastive query for that solution. We formally define some key properties that contrastive explanations must satisfy for them to be considered as valid solutions to X-DCOPs as well as theoretical results on the existence of such valid explanations. To solve X-DCOPs, we propose a distributed framework as well as several optimizations and suboptimal variants to find valid explanations. We also include a human user study that showed that users, not surprisingly, prefer shorter explanations over longer ones. Our empirical evaluations showed that our approach can scale to large problems, and the different variants provide different options for trading off explanation lengths for smaller runtimes. Thus, our model and algorithmic contributions extend the state of the art by reducing the barrier for users to understand DCOP solutions, facilitating their adoption in more real-world applications.

本研究针对现有的分布式约束优化问题（DCOP）模型面临的可解释性不足进行了探讨，提出了可解释的DCOP（X-DCOP）模型，旨在提高DCOP解决方案的可理解性。通过引入对比查询来定义有效的解释，并提供了一种分布式框架以解决X-DCOP问题，实验证明该模型在扩展性和用户偏好方面具有明显优势，促进了DCOP解决方案在现实应用中的采纳。

可解释的分布式约束优化问题