Inference of Abstraction for a Unified Account of Reasoning and Learning
<|reference_start|>Inference of Abstraction for a Unified Account of Reasoning and Learning: 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.<|reference_end|>
@article{kido2024inference,
    title={Inference of Abstraction for a Unified Account of Reasoning and Learning},
    author={Hiroyuki Kido},
    journal={arXiv preprint arXiv:2402.09046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09046},
    primaryClass={cs.AI cs.LG cs.LO}
}