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Auteur principal: Wojtowicz, Ralph
Format: Preprint
Publié: 2025
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Accès en ligne:https://arxiv.org/abs/2504.19320
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author Wojtowicz, Ralph
author_facet Wojtowicz, Ralph
contents This paper seeks to apply categorical logic to the design of artificial intelligent agents that reason symbolically about objects more richly structured than sets. Using Johnstone's sequent calculus of terms- and formulae-in-context, we develop forward chaining and normal form algorithms for reasoning about objects in cartesian categories with the rules for Horn logic. We also adapt first-order unification to support multi-sorted theories, contexts, and fragments of first-order logic. The significance of these reformulations rests in the fact that they can be applied to reasoning about objects in semantic categories that do not support classical logic or even all its connectives.
format Preprint
id arxiv_https___arxiv_org_abs_2504_19320
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Logic-Based Artificial Intelligence Algorithms Supporting Categorical Semantics
Wojtowicz, Ralph
Artificial Intelligence
03, 18
I.1.2
This paper seeks to apply categorical logic to the design of artificial intelligent agents that reason symbolically about objects more richly structured than sets. Using Johnstone's sequent calculus of terms- and formulae-in-context, we develop forward chaining and normal form algorithms for reasoning about objects in cartesian categories with the rules for Horn logic. We also adapt first-order unification to support multi-sorted theories, contexts, and fragments of first-order logic. The significance of these reformulations rests in the fact that they can be applied to reasoning about objects in semantic categories that do not support classical logic or even all its connectives.
title Logic-Based Artificial Intelligence Algorithms Supporting Categorical Semantics
topic Artificial Intelligence
03, 18
I.1.2
url https://arxiv.org/abs/2504.19320