Markov Logic: An Interface Layer for Artificial Intelligence (Synthesis Lectures on Artificial Intelligence and Machine Learning, 7) - Softcover

Pedro Domingos; Daniel Lowd

Softcover

ISBN 10: 1598296922 ISBN 13: 9781598296921

Publisher: Morgan and Claypool Publishers, 2009

Synopsis

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system.

About this title

About the Author

University of Washington

University of Oregon

Jacobs Technion-Cornell Institute at Cornell Tech

Carnegie Mellon University

University of Texas at Austin

Edition details

• Publisher: Morgan and Claypool Publishers
• Publication date: 2009
• Language: English
• ISBN 10: 1598296922
• ISBN 13: 9781598296921
• Binding: Paperback
• Edition number: 1
• Number of pages: 156