Inductive Logic Programming

Front Cover
Stephen Muggleton
Morgan Kaufmann, 1992 - Computers - 565 pages
Inductive logic programming is a new research area formed at the intersection of machine learning and logic programming. While the influence of logic programming has encouraged the development of strong theoretical foundations, this new area is inheriting its experimental orientation from machine learning. Inductive Logic Programming will be an invaluable text for all students of computer science, machine learning and logic programming at an advanced level.

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* Examination of the background to current developments within the area
* Identification of the various goals and aspirations for the increasing body of researchers in inductive logic programming
* Coverage of induction of first order theories, the application of inductive logic programming and discussion of several logic learning programs
* Discussion of the applications of inductive logic programming to qualitative modelling, planning and finite element mesh design
 

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Contents

Inductive Logic Programming
3
A Framework for Inductive Logic Programming
9
A Study of Constrained
29
Extensions of Inversion of Resolution Applied to Theory Com
63
Learning Theoretical Terms
93
193
112
Logic Program Synthesis from Good Examples
113
A Critical Comparison of Various Methods Based on Inverse
131
Efficient Learning of Logic Programs with NonDeterminate
361
An InformationBased Approach to Integrating Empirical
373
Analogical Reasoning for Logic Programming
397
Some Thoughts on Inverse Resolution
409
Department of Computer Science Katholieke Universiteit Leuven Celestij
422
Experiments in Nonmonotonic FirstOrder Induction
423
Learning Qualitative Models of Dynamic Systems
437
The Application of Inductive Logic Programming to Finite
453

NonMonotonic Learning
145
An Overview of the Interactive ConceptLearner and Theory
163
Using Confirmation The
213
Relating Relational Learning Algorithms
233
Wray Buntine
261
Efficient Induction of Logic Programs
281
Constraints for Predicate Invention
299
Refinement Graphs for FOIL and LINUS
319
282
470
Inducing Temporal Fault Diagnostic Rules from a Qualitative
473
Inductive Learning of Relations from Noisy Examples
495
Learning Chess Patterns
517
Applying Inductive Logic Programming in Reactive Environ
539
293
552
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