A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning

A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning

Philosophers frequently define knowledge as justified, true belief. We built a mathematical framework that makes it possible to define learning (increasing number of true beliefs) and knowledge of an agent in precise ways, by phrasing belief in terms of epistemic probabilities, defined from Bayes’ r...

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Bibliographic Details
Main Authors: Ola Hössjer, Daniel Andrés Díaz-Pachón, J. Sunil Rao
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Entropy
Subjects:
active information
Bayes’ rule
counterfactuals
epistemic probability
learning, justified true belief
knowledge acquisition
Online Access:https://www.mdpi.com/1099-4300/24/10/1469

Internet

https://www.mdpi.com/1099-4300/24/10/1469

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