Modal symbolic learning is the subfield of artificial intelligence that brings together machine learning and modal logic to design algorithms that extract modal logic theories from data. The generalization of model checking to multi-models and multi-formulas is key for the entire inductive process (with modal logics). We investigate such generalization by, first, pointing out the need for finite model checking in automatic inductive reasoning, and, then, showing how to efficiently solve it. We release an open-source implementation of our simulations.
Milella, M., Pagliarini, G., Paradiso, A., Stan, I. (2022). Multi-Models and Multi-Formulas Finite Model Checking for Modal Logic Formulas Induction. In Short Paper Proceedings of the 4th Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis hosted by the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022) (pp.81-85). CEUR-WS.
Multi-Models and Multi-Formulas Finite Model Checking for Modal Logic Formulas Induction
Stan I. E.
2022
Abstract
Modal symbolic learning is the subfield of artificial intelligence that brings together machine learning and modal logic to design algorithms that extract modal logic theories from data. The generalization of model checking to multi-models and multi-formulas is key for the entire inductive process (with modal logics). We investigate such generalization by, first, pointing out the need for finite model checking in automatic inductive reasoning, and, then, showing how to efficiently solve it. We release an open-source implementation of our simulations.
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