In this work, we approach the issue of privacy in distributed constraint reasoning by studying how agents compromise solution quality for preserving privacy, using utility and game theory. We propose a utilitarian definition of privacy in the context of distributed constraint reasoning, detail its different implications, and present a model and solvers, as well as their properties. We then show how important steps in a distributed constraint optimization with privacy requirements can be modeled as a planning problem, and more specifically as a stochastic game. We present experiments validating the interest of our approach, according to several criteria.

中文翻译：

---

分布式约束推理中的隐私随机博弈

在这项工作中，我们通过研究代理如何使用效用和博弈论来妥协解决方案质量以保护隐私，从而解决分布式约束推理中的隐私问题。我们在分布式约束推理的背景下提出了隐私的实用定义，详细说明了其不同的含义，并提出了一个模型和求解器，以及它们的属性。然后，我们展示了如何将具有隐私要求的分布式约束优化中的重要步骤建模为规划问题，更具体地说，可以建模为随机游戏。我们根据几个标准提出了验证我们方法的兴趣的实验。