Guessing Winning Policies in LTL Synthesis by Semantic Learning
This work addresses scalability challenges in LTL synthesis for formal verification, offering incremental improvements by integrating learning with structured logical information.
The paper tackles the problem of efficiently guessing winning strategies in parity games from LTL synthesis by using a learning-based technique that leverages semantic labeling from recent LTL-to-automata translations, enabling applications like best-effort strategies in large games and scalability improvements through easier verification and faster iteration.
We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several applications. Not only can the guessed strategy be applied as best-effort in cases where the game's huge size prohibits rigorous approaches, but it can also increase the scalability of rigorous LTL synthesis in several ways. Firstly, checking whether a guessed strategy is winning is easier than constructing one. Secondly, even if the guess is wrong in some places, it can be fixed by strategy iteration faster than constructing one from scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize exploration in the most fruitful directions. In contrast to previous works, we (i)~reflect the highly structured logical information in game's states, the so-called semantic labelling, coming from the recent LTL-to-automata translations, and (ii)~learn to reflect it properly by learning from previously solved games, bringing the solving process closer to human-like reasoning.