We consider the problem of obtaining sparse positional strategies for safety
games. Such games are a commonly used model in many formal methods, as they
make the interaction of a system with its environment explicit. Often, a
winning strategy for one of the players is used as a certificate or as an
artefact for further processing in the application. Small such certificates,
i.e., strategies that can be written down very compactly, are typically
preferred. For safety games, we only need to consider positional strategies.
These map game positions of a player onto a move that is to be taken by the
player whenever the play enters that position. For representing positional
strategies compactly, a common goal is to minimize the number of positions for
which a winning player's move needs to be defined such that the game is still
won by the same player, without visiting a position with an undefined next
move. We call winning strategies in which the next move is defined for few of
the player's positions sparse.
Unfortunately, even roughly approximating the density of the sparsest
strategy for a safety game has been shown to be NP-hard. Thus, to obtain sparse
strategies in practice, one either has to apply some heuristics, or use some
exhaustive search technique, like ILP (integer linear programming) solving. In
this paper, we perform a comparative study of currently available methods to
obtain sparse winning strategies for the safety player in safety games. We
consider techniques from common knowledge, such as using ILP or SAT
(satisfiability) solving, and a novel technique based on iterative linear
programming. The results of this paper tell us if current techniques are
already scalable enough for practical use.Comment: In Proceedings SYNT 2012, arXiv:1207.055
