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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.
https://hal.inria.fr/hal-03232354
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, May 21, 2021 - 2:58:06 PM
Last modification on : Friday, May 21, 2021 - 3:05:01 PM
Long-term archiving on: : Sunday, August 22, 2021 - 6:49:45 PM
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, May 21, 2021 - 2:58:06 PM
Last modification on : Friday, May 21, 2021 - 3:05:01 PM
Long-term archiving on: : Sunday, August 22, 2021 - 6:49:45 PM
Licence
Distributed under a Creative Commons Attribution 4.0 International License