Extending Explanation-Based Learning by Generalizing the Structure of ExplanationsExtending Explanation-Based Learning by Generalizing the Structure of Explanations presents several fully-implemented computer systems that reflect theories of how to extend an interesting subfield of machine learning called explanation-based learning. This book discusses the need for generalizing explanation structures, relevance to research areas outside machine learning, and schema-based problem solving. The result of standard explanation-based learning, BAGGER generalization algorithm, and empirical analysis of explanation-based learning are also elaborated. This text likewise covers the effect of increased problem complexity, rule access strategies, empirical study of BAGGER2, and related work in similarity-based learning. This publication is suitable for readers interested in machine learning, especially explanation-based learning. |
Contents
| 1 | |
| 15 | |
Chapter 3 A DomainIndependent Approach | 71 |
Chapter 4 An Empirical Analysis of ExplanationBased Learning | 117 |
Chapter 5 Conclusion | 147 |
Additional PHYSICS 101 Examples | 159 |
Additional BAGGER Examples | 173 |
BAGGERS Initial Inference Rules | 191 |
Statistics from Experiments | 199 |
References | 203 |
Other editions - View all
Common terms and phrases
acquired rules additional analyzing approach arbitrary number Artificial Intelligence autonomous mode BAGGER system block blockers blocks world calculation cancellation graph clear collection COMMON LISP Computer concepts Conference on Artificial consequent constraints construct contains determine disjuncts domain theory eliminated equation schemata experiments explanation structure expression external forces Fext Figure final Fnet focus rule formulae G. F. DeJong goal implementation inference rules instantiation inter-object forces INTERLISP intermediate involves knowledge learned rule learning systems left-hand side Machine Learning mathematical momentum moved multiple NO-LEARN node object obstacleSet operator performance PHYSICS 101 preconditions predicate predicate calculus presents primary obstacles problem solver Proceedings produces proof recurrence recursive requires rule applications rules learned sample problem satisfy secondary obstacles sequence sequential rule situation calculus solving special-case specific example specific problem STD-EBL step Strategy structure of explanations subexplanations table2 technique tower-building training mode unwindable rules variables vector