7 research outputs found
Decidability Results for Multi-objective Stochastic Games
We study stochastic two-player turn-based games in which the objective of one
player is to ensure several infinite-horizon total reward objectives, while the
other player attempts to spoil at least one of the objectives. The games have
previously been shown not to be determined, and an approximation algorithm for
computing a Pareto curve has been given. The major drawback of the existing
algorithm is that it needs to compute Pareto curves for finite horizon
objectives (for increasing length of the horizon), and the size of these Pareto
curves can grow unboundedly, even when the infinite-horizon Pareto curve is
small. By adapting existing results, we first give an algorithm that computes
the Pareto curve for determined games. Then, as the main result of the paper,
we show that for the natural class of stopping games and when there are two
reward objectives, the problem of deciding whether a player can ensure
satisfaction of the objectives with given thresholds is decidable. The result
relies on intricate and novel proof which shows that the Pareto curves contain
only finitely many points. As a consequence, we get that the two-objective
discounted-reward problem for unrestricted class of stochastic games is
decidable.Comment: 35 page