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Logic meets probability and learning: a tutorial

Year: 2024

Type: article

Abstract: This is a tutorial given at the Logic for AI Spring School at Lake Como: https://lais.lakecomoschool.org This is the first part of the tutorial and covers the foundations of propositional logic and probability mainly through the lens of weighted model counting. It then covers results on continuous data with inductive logic programming, tractable probabilistic models, and other avenues for learning. Finally, it covers using logic for programming machine learning models and recent extensions to weighted model counting such as algebraic model counting.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13693912

Author: Belle, Vaishak

Language: English

Topic: Logic, Reasoning, and Knowledge

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949406924

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