Inference of abstraction for grounded predicate logic

Kido, Hiroyuki ORCID: https://orcid.org/0000-0002-7622-4428 2025. Inference of abstraction for grounded predicate logic. Presented at: Twelfth Annual Conference on Advances in Cognitive Systems, Atlanta, Georgia, USA, 13-15 October 2025. Item availability restricted.

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Abstract

An important open question in AI is what simple and natural principle enables a machine to ground logical reasoning in data for meaningful abstraction. This paper explores a conceptually new approach to combining probabilistic reasoning and predicative symbolic reasoning over data. We return to the era of reasoning with a full joint distribution before the advent of Bayesian networks. We then discuss that a full joint distribution over models of exponential size in propositional logic and of infinite size in predicate logic should be simply derived from a full joint distribution over data of linear size. We show that the same process is not only enough to generalise the logical consequence relation of predicate logic but also to provide a new perspective to rethink well-known limitations such as the undecidability of predicate logic, the symbol grounding problem and the principle of explosion. The reproducibility of this theoretical work is fully demonstrated by the included proofs.

Item Type:	Conference or Workshop Item (Paper)
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Related URLs:	http://cogsys.org/conference/2025/
Date of First Compliant Deposit:	22 September 2025
Date of Acceptance:	17 September 2025
Last Modified:	23 Sep 2025 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/181260

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