Inductive Logic ProgrammingInductive 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. * * 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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Common terms and phrases
added addition algorithm allows applied approach arguments Artificial Intelligence assume atoms background knowledge base body called CIGOL CLINT complete Computer concept consistent constrained atoms constraint constructed contains corresponding covers defined definition dependency derivation described domain theory efficient exists explanation expression extend facts Figure first-order FOCL FOIL function gain given GOLEM ground head Horn clauses hypothesis implied induction inference input instances integrity International introduced inverse resolution knowledge knowledge base language least limit LINUS literals Logic Programming Machine Learning means method Morgan Kaufmann Muggleton negative examples non-monotonic Note occur operator performance positive positive examples possible predicate present problem Proceedings proof prove reasoning relation representation representative resolution respect restricted result RLGG rule similar sorted space specialization specific step structure substitution Theorem theory tree true values variables
References to this book
Bibliographic information
| Title | Inductive Logic Programming Issue 38 of A.P.I.C. studies in data processing, ISSN 0067-2483 Issue 38 of APIC Series, Automatic Programming Information Centre Brighton Volume 38 of Automatic Programming Information Centre Brighton: The APIC series |
| Editor | Stephen Muggleton |
| Edition | illustrated |
| Publisher | Morgan Kaufmann, 1992 |
| ISBN | 0125097158, 9780125097154 |
| Length | 565 pages |
| Subjects | › › Computers / Intelligence (AI) & Semantics Computers / Programming / Object Oriented |
| Export Citation | BiBTeX EndNote RefMan |