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We provide a learning-based technique for guessing a winning strategy in aparity game originating from an LTL synthesis problem. A cheaply obtained guesscan be useful in several applications. Not only can the guessed strategy beapplied as best-effort in cases where the game's huge size prohibits rigorousapproaches, but it can also increase the scalability of rigorous LTL synthesisin several ways. Firstly, checking whether a guessed strategy is winning iseasier than constructing one. Secondly, even if the guess is wrong in someplaces, it can be fixed by strategy iteration faster than constructing one fromscratch. Thirdly, the guess can be used in on-the-fly approaches to prioritizeexploration in the most fruitful directions.  In contrast to previous works, we (i)~reflect the highly structured logicalinformation in game's states, the so-called semantic labelling, coming from therecent LTL-to-automata translations, and (ii)~learn to reflect it properly bylearning from previously solved games, bringing the solving process closer tohuman-like reasoning.

Guessing Winning Policies in LTL Synthesis by Semantic Learning