How do we remember the past in randomised strategies?

TL;DR

This paper investigates the impact of different definitions of randomised strategies in infinite graph games, revealing that strategy strength and memory requirements critically affect winning conditions and decidability.

Contribution

It analyzes how various notions of randomised strategies influence outcomes in infinite graph games, highlighting differences in winning probabilities, memory needs, and decidability issues.

Findings

Almost-surely winning can become losing under weaker strategies.

Infinite memory is often required for strategy translation.

Decidability problems arise for the strongest strategies.

Abstract

Graph games of infinite length are a natural model for open reactive processes: one player represents the controller, trying to ensure a given specification, and the other represents a hostile environment. The evolution of the system depends on the decisions of both players, supplemented by chance. In this work, we focus on the notion of randomised strategy. More specifically, we show that three natural definitions may lead to very different results: in the most general cases, an almost-surely winning situation may become almost-surely losing if the player is only allowed to use a weaker notion of strategy. In more reasonable settings, translations exist, but they require infinite memory, even in simple cases. Finally, some traditional problems becomes undecidable for the strongest type of strategies.