Generative Logic with Time: Beyond Logical Consistency and Statistical Possibility

This paper gives a simple theory of inference to logically reason symbolic knowledge fully from data over time. We take a Bayesian approach to model how data causes symbolic knowledge. Probabilistic reasoning with symbolic knowledge is modelled as a process of going the causality forwards and backwa...

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Bibliographic Details
Published in:arXiv.org 2023-03
Main Author: Kido, Hiroyuki
Format: Article
Language:English
Subjects:
Axioms
Consistency
Maximum likelihood estimation
Probabilistic models
Statistical analysis
Statistical inference
Online Access:Get full text
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Summary:This paper gives a simple theory of inference to logically reason symbolic knowledge fully from data over time. We take a Bayesian approach to model how data causes symbolic knowledge. Probabilistic reasoning with symbolic knowledge is modelled as a process of going the causality forwards and backwards. The forward and backward processes correspond to an interpretation and inverse interpretation of formal logic, respectively. The theory is applied to a localisation problem to show a robot with broken or noisy sensors can efficiently solve the problem in a fully data-driven fashion.
ISSN:2331-8422