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Conference papers

Learning Automata with Side-Effects

Abstract : Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability, and hence optimizations that enable learning of compact representations are important. This paper exploits monads, both as a mathematical structure and a programming construct, to design and prove correct a wide class of such optimizations. Monads enable the development of a new learning algorithm and correctness proofs, building upon a general framework for automata learning based on category theory. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. We show that this allows us to uniformly capture existing algorithms, develop new ones, and add optimizations.
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Submitted on : Friday, May 21, 2021 - 2:58:06 PM
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Gerco Heerdt, Matteo Sammartino, Alexandra Silva. Learning Automata with Side-Effects. 15th International Workshop on Coalgebraic Methods in Computer Science (CMCS), Apr 2020, Dublin, Ireland. pp.68-89, ⟨10.1007/978-3-030-57201-3_5⟩. ⟨hal-03232354⟩



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