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 Study of Constrained
29
Extensions of Inversion of Resolution Applied to Theory Com
63
Learning Theoretical Terms
93
Logic Program Synthesis from Good Examples
113
A Critical Comparison of Various Methods Based on Inverse
131
NonMonotonic Learning
145
An Overview of the Interactive ConceptLearner and Theory
163
Representation and Learning Model
339
The RDT Algorithm
345
RDT and MOBAL
353
Future Research
354
Efficient Learning of Logic Programs with NonDeterminate NonDiscriminating Literals
361
B Kijsirikul M Numao and M Shimura 1 Introduction
362
Overview of CHAM
364
Learning of Sort Programs
367

A Framework for Inductive Logic Programming
193
P A Flach 1 Introduction
194
Induction of Strong Theories
198
Concept Learning from Incomplete Examples
200
Induction of Weak Theories
208
Conclusions
210
Using Confirmation The ory and NonMonotonic Logics for Incremental Learning
213
Gabbay D Gillies A Hunter S Muggleton Y Ng and B Richards 1 Introduction
214
Outline of the Project
223
Discussion
225
Implementations
231
Relating Relational Learning Algorithms
233
An Organization for Relational Learning Algorithms
236
Potential Research Directions
251
Conclusions
253
Machine Invention of FirstOrder Predicates by Inverting Resolution
261
S Muggleton and W Buntine 1 Introduction
262
CIGOL Sessions
263
Preliminaries
265
Inverting Resolution
269
CIGOL
274
Discussion
277
Efficient Induction of Logic Programs
281
S Muggleton and C Feng 1 Introduction
282
Relative Least General Generalizations
284
Restricted Forms of Background Knowledge
287
Restrictions on the Hypothesis Language
289
Clause Reduction
293
Implementation and Results
294
Conclusions
297
Constraints for Predicate Invention
299
R Wirth and P ORorke 1 Introduction
300
The Method
302
Examples
309
Related Work
313
Current Status Limitations and Future Work
315
Conclusions
316
Refinement Graphs for FOIL and LINUS
319
S Džeroski and N Lavrač 1 Introduction
320
Refinement Operators for FOIL and LINUS
321
Searching Refinement Graphs
327
Summary
331
Controlling the Complexity of Learning in Logic through Syn tactic and TaskOriented Models
335
JU Kietz and S Wrobel 1 Introduction
336
Dimensions of Controlling Complexity
337
Experiments and Results
370
Conclusions
371
An InformationBased Approach to Integrating Empirical and ExplanationBased Learning
373
J Pazzani C A Brunk and G Silverstein 1 Introduction
374
FOIL
375
FOCL
377
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
Results
459
Conclusions
461
Inducing Temporal Fault Diagnostic Rules from a Qualitative Model
473
Feng 1 Introduction
474
A Description of the Power Subsystem
476
Temporal Representation
479
Inducing Temporal Fault Diagnostic Rules
480
Conclusions
486
Inductive Learning of Relations from Noisy Examples
495
N Lavrač and S Džeroski 1 Introduction
496
Defining Learning in LINUS and FOIL
497
The LINUS Algorithm
500
Learning from Imperfect Data
503
Noise Handling in LINUS and FOIL
505
Experiments with NonNoisy Data
507
Noisy Data
509
Summary and Discussion
512
Learning Chess Patterns
517
E Morales 1 Introduction
518
Constrained RLGG
520
Perturbation Method
524
The Learning Algorithm
526
Related Work
528
Examples
529
Conclusions and Future Research Directions
531
Applying Inductive Logic Programming in Reactive Environ ments
539
Hume and C Sammut 1 The Problem
540
Overview of CAP
541
Constructing Initial Theories from Observations
542
The Global Learning Strategy
544
Conclusions
548
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