Fairness in Multi-Agent Sequential Decision-Making
Fairness in Multi-Agent Sequential Decision-Making
复制标题
DOI：
--
复制
发表时间：
2014-12
期刊：
影响因子：
--
通讯作者：
Chongjie Zhang;J. Shah
中科院分区：
文献类型：
--
作者：
Chongjie Zhang;J. Shah
对于具有局部利益的多智能体决策问题，我们定义了一个公平解准则。这一新的准则旨在最大化代理的最差性能，同时考虑整体性能。我们开发了一种简单的线性规划方法和一种更具可扩展性的博弈论方法来计算最优公平策略。这种博弈论方法将公平优化问题描述为一个两人零和博弈，并使用迭代算法来寻找与最优公平策略相对应的纳什均衡。我们通过利用问题结构和价值函数近似来扩大这种方法的规模。我们在资源分配问题上的实验表明，这种公平准则比效用准则提供了更有利的解决方案，并且我们的博弈论方法比线性规划方法要快得多。
We define a fairness solution criterion for multi-agent decision-making problems, where agents have local interests. This new criterion aims to maximize the worst performance of agents with a consideration on the overall performance. We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy. This game-theoretic approach formulates this fairness optimization as a two-player zero-sum game and employs an iterative algorithm for finding a Nash equilibrium, corresponding to an optimal fairness policy. We scale up this approach by exploiting problem structure and value function approximation. Our experiments on resource allocation problems show that this fairness criterion provides a more favorable solution than the utilitarian criterion, and that our game-theoretic approach is significantly faster than linear programming.