Multiagent Cooperative Learning Strategies for Pursuit-Evasion Games

Guaranteed Strategies for nonlinear Multi-Player Pursuit-Evasion Games

In this paper, we provide a methodology to design strategies for either guaranteed capture or guaranteed evasion in the case of pursuit-evasion games with multiple players which are represented by nonlinear dynamic models. This methodology is based on the continuously differentiable upper and lower approximations of the minimum and maximum function of an arbitrary number of arguments, compariso...

Pursuit- Evasion Games

Two players, Red and Blue, each independently choose a vertex of a connected graph G. Red must then pay Blue an amount equal to the distance between the vertices chosen. In this note, w e investigate the value v(G) of this pursuit-evasion game for various classes of graphs G, as well as those optimal mixed strategies for achieving v(G). It is shown that some rather counterintuitive behavior can...

Self-learning fuzzy logic controllers for pursuit-evasion differential games

This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. The system learns autonomously without supervision or a priori training data. Two novel techniques are proposed. The first technique combines Q(λ)-learning with function approximation (fuzzy inference system) to tune the parameters of a fuzzy logic controller operating in continuous state...

Multiagent Pursuit Evasion, or Playing Kabaddi

We study a version of pursuit evasion where two or more pursuers are required to capture the evader because the evader is able to overpower a single defender. The pursuers must coordinate their moves to fortify their approach against the evader while the evader maneuvers to disable pursuers from their unprotected sides. We model this situation as a game of Kabaddi, a popular South Asian sport w...

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