Deciding Maxmin Reachability in Half-Blind Stochastic Games

TL;DR

This paper investigates the decidability of maxmin reachability in half-blind stochastic games, introducing a class called leaktight games where the problem becomes decidable, and analyzing the strategic complexities involved.

Contribution

The paper defines leaktight half-blind games, proving decidability of maxmin reachability, and explores the strategic advantages of mixed strategies and infinite-memory requirements.

Findings

Decidability is achieved within leaktight half-blind games.

Mixed strategies can be more powerful than pure strategies.

Optimal minimizer strategies may require infinite memory.

Abstract

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.