Inference of Abstraction for a Unified Account of Reasoning and Learning

Hiroyuki Kido

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arXiv · 2402.09046

Inference of Abstraction for a Unified Account of Reasoning and Learning

Hiroyuki Kido

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Abstract

Inspired by Bayesian approaches to brain function in neuroscience, we give a simple theory of probabilistic inference for a unified account of reasoning and learning. We simply model how data cause symbolic knowledge in terms of its satisfiability in formal logic. The underlying idea is that reasoning is a process of deriving symbolic knowledge from data via abstraction, i.e., selective ignorance. The logical consequence relation is discussed for its proof-based theoretical correctness. The MNIST dataset is discussed for its experiment-based empirical correctness.

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Hiroyuki Kido. 2024-02-14. Inference of Abstraction for a Unified Account of Reasoning and Learning. https://arxiv.org/abs/2402.09046

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