Inductive Logic ProgrammingStephen Muggleton Inductive logic programming is a new research area emerging at present. Whilst inheriting various positive characteristics of the parent subjects of logic programming an machine learning, it is hoped that the new area will overcome many of the limitations of its forbears. This book describes the theory, implementations and applications of Inductive Logic Programming. |
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... Sorted and Constraint Generalization Problems 32 4 Computing Sorted Generalization . . 40 5 Computing Constraint Generalization 44 6 7 The Learnability of Sorted and Constrained Atoms Analysis and Conclusions 48 53 8 56 Relationship to ...
... Sorted and Constraint Generalization Problems 32 4 Computing Sorted Generalization . . 40 5 Computing Constraint Generalization 44 6 7 The Learnability of Sorted and Constrained Atoms Analysis and Conclusions 48 53 8 56 Relationship to ...
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Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
Constraints for Predicate Invention | 14 |
Inducing Temporal Fault Diagnostic Rules from a Qualitative | 24 |
A Study of Constrained | 29 |
2 | 30 |
4 | 35 |
7 | 45 |
Related Work | 313 |
Refinement Graphs for FOIL and LINUS | 319 |
4 | 327 |
Controlling the Complexity of Learning in Logic through Syn | 336 |
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 |
Extensions of Inversion of Resolution Applied to Theory Com | 63 |
879 | 77 |
Ranan B Banerji | 93 |
3 | 98 |
Logic Program Synthesis from Good Examples | 113 |
A Critical Comparison of Various Methods Based on Inverse | 131 |
4 | 139 |
NonMonotonic Learning | 145 |
An Overview of the Interactive ConceptLearner and Theory | 163 |
5 | 179 |
Richards | 214 |
Relating Relational Learning Algorithms | 233 |
Machine Invention of FirstOrder Predicates by Inverting | 261 |
Integrating Abduction and Induction | 300 |
4 | 306 |
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 |
Model | 471 |
4 | 480 |
4 | 502 |
8 | 510 |
Learning Chess Patterns | 517 |
Applying Inductive Logic Programming in Reactive Environ | 539 |
5 | 547 |
551 | |
559 | |
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Common terms and phrases
0-subsumes absorption abstraction operators Algorithmic Learning Theory applied approach arguments arity Artificial Intelligence background knowledge body Buntine Cā Cā CIGOL CLINT Closed World Assumption complete Computer concept descriptions constrained atoms constraint predicates constraint theory constructed contains defined definite clauses described domain theory eā efficient facts finite first-order first-order logic flattening FOCL formula framework function symbols given GOLEM ground clause head heuristic Horn clauses hypothesis implied inductive learning Inductive Logic Programming inference input instances instantiation integrity constraints intended interpretation inverse resolution knowledge base learnable learning algorithms Lemma literals Machine Learning method Morgan Kaufmann Muggleton negative examples non-monotonic logic oracle PAC-learning polynomial positive examples problem Proceedings Prolog proof tree relation representation restricted RLGG Rouveirol rules saturation Section set of clauses Shapiro skolemized sort theory sorted atoms sparky specific subset substitution target Theorem tion true truncation tuples unit clauses variables
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Page ix - Department of Information and Computer Science University of California, Irvine, CA 92717 Abstract As methodologies and tools for chip-level design mature, design effort becomes focused on higher abstraction levels.ā