Playing Stochastically in Weighted Timed Games to Emulate Memory

TL;DR

This paper demonstrates that in divergent weighted timed games, stochastic strategies can replicate the optimal deterministic strategies without requiring memory, simplifying the strategic complexity.

Contribution

It introduces stochastic decision models in weighted timed games and proves their equivalence to deterministic strategies in certain classes, removing the need for memory.

Findings

Stochastic strategies can match deterministic optimal values in divergent weighted timed games.

Expected value in stochastic strategies equals classical value in specific game classes.

Stochastic decision-making can replace memory-based strategies in these games.

Abstract

Weighted timed games are two-player zero-sum games played in a timed automaton equipped with integer weights. We consider optimal reachability objectives, in which one of the players, that we call Min, wants to reach a target location while minimising the cumulated weight. While knowing if Min has a strategy to guarantee a value lower than a given threshold is known to be undecidable (with two or more clocks), several conditions, one of them being divergence, have been given to recover decidability. In such weighted timed games (like in untimed weighted games in the presence of negative weights), Min may need finite memory to play (close to) optimally. This is thus tempting to try to emulate this finite memory with other strategic capabilities. In this work, we allow the players to use stochastic decisions, both in the choice of transitions and of timing delays. We give a definition of the expected value in weighted timed games. We then show that, in divergent weighted timed games as well as in (untimed) weighted games (that we call shortest-path games in the following), the stochastic value is indeed equal to the classical (deterministic) value, thus proving that Min can guarantee the same value while only using stochastic choices, and no memory.