In an inverse game problem, one needs to infer the cost function of the players in a game such that a desired joint strategy is a Nash equilibrium. We study the inverse game problem for a class of multiplayer matrix games, where the cost perceived by each player is corrupted by random noise. We provide sufficient conditions for the players' quantal response equilibrium -- a generalization of the Nash equilibrium to games with perception noise -- to be unique. We develop efficient optimization algorithms for inferring the cost matrix based on semidefinite programs and bilevel optimization. We demonstrate the application of these methods using examples where we design the cost matrices that rationalize collision avoidance in path-planning and fairness in resource allocation.
翻译:在反向的游戏问题中,人们需要推断游戏中玩家的成本功能,这样,一个理想的联合战略就是纳什均衡。我们研究一组多玩家矩阵游戏的反向游戏问题,每个玩家所认为的成本被随机噪音所腐蚀。我们为玩家的四轮反应平衡提供了充分的条件 -- -- 将纳什平衡与有感知噪音的游戏进行概括化 -- -- 成为独特的。我们开发了高效优化算法,用以根据半定式的程序和双级优化来推断成本矩阵。我们用我们设计成本矩阵的例子展示了这些方法的应用,这些成本矩阵使得在路径规划中避免碰撞和资源分配的公平性合理化。