Inference of abstraction for human-like logical reasoning

Kido, Hiroyuki ORCID: https://orcid.org/0000-0002-7622-4428 2025. Inference of abstraction for human-like logical reasoning. Presented at: 4th Advanced Course and Symposium on Artificial Intelligence & Neuroscience (ACAIN), Tuscany, Italy, 22-25 September 2024. Published in: Nicosia, G., Ojha, V., Giesselbach, S., Pardalos, M. P. and Umeton, R. eds. Machine Learning, Optimization, and Data Science. Lecture Notes in Computer Science , vol.15510 Springer, pp. 191-206. 10.1007/978-3-031-82487-6_14

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Abstract

Inspired by empirical work in neuroscience for Bayesian approaches to brain function, we give a unified probabilistic account of various types of symbolic reasoning from data. We characterise them in terms of formal logic using the classical consequence relation, an empirical consequence relation, maximal consistent sets, maximal possible sets and maximum likelihood estimation. The theory gives new insights into reasoning towards human-like machine intelligence.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Springer
ISBN:	978-3-031-82486-9
Date of First Compliant Deposit:	26 June 2024
Date of Acceptance:	1 June 2024
Last Modified:	02 Apr 2025 13:10
URI:	https://orca.cardiff.ac.uk/id/eprint/170135

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