Fighting m-criminals with Bayesian Stackelberg games
نویسنده
چکیده
Bayesian Stackelberg games have been used to compute optimal patrol strategies for the security of the Los Angeles International Airport (LAX) by modelling the security situation as a 1-leader, 1-follower, Bayesian game where there is one leader type and an arbitrary number of follower types. A previous approach, named DOBSS, computes the Stackelberg solution for this game by solving a Mixed Integer Linear Program (MILP). This paper shows that the action-graph game representation would be helpful in speeding up the MILP algorithm and also make it possible to scale the MILP approach to n-followers, each with an arbitrary number of follower types, as long as the followers are unable to affect each other by their actions.
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