Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming

Balios - The Engine for Bayesian Logic Programs

Inductive Logic Programming (ILP) [4] combines techniques from machine learning with the representation of logic programming. It aims at inducing logical clauses, i.e, general rules from specific observations and background knowledge. Because of focusing on logical clauses, traditional ILP systems do not model uncertainty explicitly. On the other hand, state-of-the-art probabilistic models such...

Statistical Relational Learning: An Inductive Logic Programming Perspective

In the past few years there has been a lot of work lying at the intersection of probability theory, logic programming and machine learning [14, 18, 13, 9, 6, 1, 11]. This work is known under the names of statistical relational learning [7, 5], probabilistic logic learning [4], or probabilistic inductive logic programming. Whereas most of the existing works have started from a probabilistic lear...

Towards Combining Inductive Logic Programming with Bayesian Networks

Learning Stochastic Logic Programs

Stochastic Logic Programs (SLPs) have been shown be a generalisation of Hidden Markov Models (HMMs), stochastic context-free grammars, and directed Bayes’ nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first-order range-restricted definite clause. This paper summarises the syntax, distributional semantics and proof techniqu...

Induction as a Search Procedure

This chapter introduces Inductive Logic Programming from the perspective of search algorithms in Computer Science. It first briefly considers the Version Spaces approach to induction, and then focuses on Inductive Logic Programming: from its formal definition and main techniques and strategies, to priors used to restrict the search space and optimized sequential, parallel, and stochastic algori...