A Delta Debugger for ILP Query Execution

Because query execution is the most crucial part of Inductive Logic Programming (ILP) algorithms, a lot of effort is invested in developing faster execution mechanisms. These execution mechanisms typically have a low-level implementation, making them hard to debug. Moreover, other factors such as the complexity of the problems handled by ILP algorithms and size of the code base of ILP data mining systems make debugging at this level a very difficult job. In this work, we present the trace-based debugging approach currently used in the development of new execution mechanisms in hipP, the engine underlying the ACE Data Mining system. This debugger uses the delta debugging algorithm to automatically reduce the total time needed to expose bugs in ILP execution, thus making manual debugging step much lighter.Comment: Paper presented at the 16th Workshop on Logic-based Methods in Programming Environments (WLPE2006

Implementation of Breadth-First Search Method Based on a Randomly Chosen Bottom Clause for Inductive Logic Programming Method

University of Minnesota M.S. thesis. 2017. Major: Computer Science. Advisor: Dr. Richard Maclin. 1 computer file (PDF); 70 pages.Inductive Logic Programming (ILP) is the study of learning methods for data and rules that are represented in first-order predicate logic [Muggleton]. ILP methods mostly use logic programming as a uniform representation language for examples, background knowledge and hypotheses. Background knowledge holds the information about the language used to describe the examples and concepts, such as possible values of variables, hierarchies, predicates, and rules. ILP induces hypotheses from examples represented as first-order predicates and synthesize new knowledge from the examples. There are two standard approaches in ILP, one is bottom-up and second is top-down. Bottom-up programs implemented in systems such as ALEPH (A Learning Engine Processing Hypothesis) start with a very specific clause (also called a bottom clause) generated from a seed positive example and generalize it as far as possible without covering negative examples. The purpose of ILP is to discover definition of target predicates together with background knowledge such that it entails positive examples and not negative examples. The aim of this research is to implement a bottom-up learning mechanism incorporating a bottom clause for implementing Inductive Logic Programming methods using standard DBMS software to represent data and a Java interface to implement the ILP methods

Specification of knowledge acquisition and modeling of the process of the consensus

zhdanova2004aIn this deliverable, specification of knowledge acquisition and modeling of the process of consensus is provided

A brief introduction to inductive logic programming

Invited Tutorialstatus: publishe

The logic of learning: a brief introduction to Inductive Logic Programming

This paper is intended to provide an introduction to ILP. We will both review some of the established approaches to Horn clause induction (Section 2), and recent work on induction of integrity constraints (Section 3). 2 Horn clause inductio