Distributed pursuit algorithms for probabilistic adversaries on connected graphs

Abstract: A gambler moves between the vertices $1, \ldots, n$ of a graph using the probability distribution $p_{1}, \ldots, p_{n}$. Multiple cops pursue the gambler on the graph, only being able to move between adjacent vertices. We investigate the expected capture time for the gambler against $k$ cops as a function of $n$ and $k$ for three versions of the game: (1) known gambler: the cops know the gambler's distribution (2) unknown gambler: the cops do not know the gambler's distribution (3) known changing gambler: the gambler's distribution can change every turn, but the cops know all of the gambler's distributions from the beginning. We show for $n > k$ that if the cops are allowed to choose their initial positions before the game starts and before they know the gambler's distribution(s), and if both the gambler and the cops play optimally, then the expected capture time is $\Theta(n/k)$ for the known gambler, the unknown gambler, and the known changing gambler.