Deciding Maxmin Reachability in Half-Blind Stochastic Games

Edon Kelmendi (1) , Hugo Gimbert (1)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique

Two-player, turn-based, stochastic games with reachability conditions are considered, where the maximizer has no information (he is blind) and is restricted to deterministic strategies whereas the minimizer is perfectly informed. We ask the question of whether the game has maxmin 1, in other words we ask whether for all \epsilon > 0 there exists a deterministic strategy for the (blind) maximizer such that against all the strategies of the minimizer, it is possible to reach the set of final states with probability larger than 1 − \epsilon. This problem is undecidable in general, but we define a class of games, called leaktight half-blind games where the problem becomes decidable. We also show that mixed strategies in general are stronger for both players and that optimal strategies for the minimizer might require infinite-memory.

Mots clés: leaktight; stochastic games; half-blind; maxmin reachability; partial-information

Domaines: Informatique et théorie des jeux [cs.GT]

Edon Kelmendi, Hugo Gimbert. Deciding Maxmin Reachability in Half-Blind Stochastic Games. 2016. ⟨hal-01320391⟩