This paper addresses the control of a team of autonomous agents pursuing a smart evader in a non-accurately mapped terrain. By describing the problem as a partial information Markov game, we are able to integrate maplearning and pursuit. We propose receding horizon control policies, in which the pursuers and the evader try to respectively maximize and minimize the probability of capture at the next time instant. Because this probability is conditioned to distinct observations for each team, the resulting game is nonzero-sum. When the evader has access to the pursuers' information, we show that a Nash solution to the one-step nonzero-sum game always exists. Moreover, we propose a method to compute the Nash equilibrium policies by solving an equivalent zero-sum matrix game. A simulation example shows the feasibility of the proposed approach.
There exist a lot of works based on game theoretic principles regarding the PE problem such as in REF where the author described the control of an autonomous agents' team tracking an intelligent evader in a nonaccurately mapped terrain based on a method to calculate the Nash equilibrium policies by resolving an equivalent zero-sum matrix game.
Probabilistic pursuit-evasion games: a one-step Nash approach
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)
Computer Science