Small Strategies for Safety Games
We consider safety games on finite, edge-labeled graphs and present an algorithm based on automata learning to compute small strategies. Our idea is as follows: we incrementally learn regular sets of winning plays until a winning strategy can be derived. For this purpose we develop a modified version of Kearns and Vazirani’s learning algorithm. Since computing a minimal strategy in this setting is hard (we prove that the corresponding decision problem is NP-complete), our algorithm, which runs in polynomial time, is an interesting and effective heuristic that yields small strategies in our experiments.
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