Aimed at graduate and research-level students in machine learning, knowledge acquisition, and general artificial intelligence, this book reports on an approach to the automation of theory revision, an area studied in sub-divisions of artificial intelligence. This approach is based mainly on machine learning, but also borrows some ideas from deductive databases and logic programming. The book develops a framework for interactive concept learning in knowledge-based systems, examining the methodology of an integrated learning system named Clint.

Interactive Theory Revision: An Inductive Logic Programming Approach (Knowledge-Based Systems Series)

Computers & Technology

De Raedt, Luc

Software

Academic Pr

An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning, 32)

Kersting, Kristian

Natarajan, Sriraam

Computer Science

Morgan & Claypool Publishers

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Morgan Claypool

POOLE, David

Springer

This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.

Logical and Relational Learning (Cognitive Technologies)

The first textbook ever to cover multi-relational data mining and inductive logic programming, this book fully explores logical and relational learning. Ideal for graduate students and researchers, it also looks at statistical relational learning.

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Books by De Raedt, Luc