Machine Learning Proceedings 1992

Description

Front Cover 2
Machine Learning 3
Copyright Page 4
Table of Contents 5
Preface 9
Program Committee 10
ML 92 Informal Workshop Themes and Coordinators 10
Chapter 1. Generalizing from Case Studies: A Case Study 11
- Abstract 11
- 1 PROBLEM AND OBJECTIVES 11
- 2 GENERALIZING CASE STUDIES 12
- 3 AN APPLICATION 13
- 4 LIMITATIONS 19
- References 20
- 5 CONCLUSION 20

Front Cover 2
Machine Learning 3
Copyright Page 4
Table of Contents 5
Preface 9
Program Committee 10
ML 92 Informal Workshop Themes and Coordinators 10
Chapter 1. Generalizing from Case Studies: A Case Study 11
- Abstract 11
- 1 PROBLEM AND OBJECTIVES 11
- 2 GENERALIZING CASE STUDIES 12
- 3 AN APPLICATION 13
- 4 LIMITATIONS 19
- References 20
- 5 CONCLUSION 20
- Acknowledgements 20
Chapter 2. On Learning More Concepts 21
- 1 INTRODUCTION 21
- Abstract 21
- 4 THE MULTI-BALLS LEARNING ALGORITHM 23
- 3 UPPER BOUND ON COVERAGE 23
- 5 THE LARGE-BALL LEARNING ALGORITHM 26
- 6 COVERAGE OF CURRENT LEARNING ALGORITHMS 27
- 7 DISCUSSION 28
- Acknowledgements 28
- References 28
Chapter 3. The Principal Axes Method for Constructive Induction 30
- 1 INTRODUCTION 30
- Abstract 30
- 2 LEARNING PRINCIPAL AXES 31
- 4 GENERATING SIMILARITY MATRIX 32
- 3 DISTANCE METRIC 32
- 5 DESCRIPTION SPACE TRANSFORMATION 33
- 7 SUMMARY 35
- 6 EMPIRICAL EVALUATION 35
- Acknowledgments 36
- References 37
Chapter 4. Learning by Incomplete Explanations of Failures in Recursive Domains 40
- 1 Introduction 40
- 2 Means-ends analysis search in recursive domains 40
- Abstract 40
- 3 Problem solving and learning in FS2 41
- 4 Experimental results 43
- 5 Related work 44
- 6 Conclusions and future work 45
- References 45
Chapter 5. Eliminating Redundancy in Explanation-Based Learning 47
- Abstract 47
- 1 INTRODUCTION 47
- 3 EXAMPLE-GUIDEDUNFOLDING 48
- 2 PRELIMINARIES 48
- 5 RELATED WORK 50
- 4 EXPERIMENTAL RESULTS 50
- 6 CONCLUDING REMARKS 51
- References 51
Chapter 6. Trading off Consistency and Efficiency in Version-Space Induction 53
- Abstract 53
- 1 INTRODUCTION 53
- 2 LEARNING WITH VARIABLE-FACTORED CONJUNCTIVE CONCEPT LANGUAGES 54
- 3 THE FCE LEARNING ALGORITHM 54
- 4 UTILITY 56
- Acknowledgements 58
- 5 RELATION TO INDUCTIVE LANGUAGE SHIFT 58
- References 58
- 6 CONCLUSION 58
Chapter 7. Peepholing: choosing attributes efficiently for megainduction 59
- Abstract 59
- 1 INTRODUCTION AND MOTIVATION 59
- 2 PEEPHOLING 60
- 3 SHORTLISTING 60
- 4 BLINKERING 62
- 5 EMPIRICAL EVALUATION 63
- References 64
- 6 CONCLUSIONS 64
- Acknowledgements 64
Chapter 8. Improving Path Planning with Learning 65
- Abstract 65
- 1 INTRODUCTION 65
- 2 ALGORITHM 65
- 3 GENERAL ANALYSIS 67
- 4 SPECIFIC CASE ANALYSIS 69
- 5 COMPUTATIONAL EXPERIENCE 70
- Acknowledgements 71
- 6 FUTURE WORK 71
- References 71
- 7 CONCLUSION 71
CHAPTER 9. THE RIGHT REPRESENTATION FOR DISCOVERY: FINDING THE CONSERVATION OF MOMENTUM 72
- Abstract 72
- 1 INTRODUCTION 72
- 3 CONVENTIONAL MATHEMATICAL APPROACH 73
- 2 CONSERVATION OF MOMENTUM 73
- 4 THE DIAGRAMMATIC APPROACH 76
- 5 DISCUSSION 80
- References 81
- 6 CONCLUSIONS 81
- Acknowledgements 81
Chapter 10. Learning to Predict in Uncertain Continuous Tasks 82
- 1 Introduction 82
- Abstract 82
- 3 Manipulation Tasks 83
- 2 Assumptions 83
- 6 Funnels 84
- 4 Noise and Uncertainty 84
- 5 Generalization 84
- 7 Learning Funnels 85
- 8 Experiments 88
- 9 Assumptions Revisited 90
- References 91
- Acknowledgements 91
Chapter 11. Lazy Partial Evaluation: An Integration of Explanation-Based Generalisation and Partial Evaluation 92
- 1 Introduction 92
- Abstract 92
- 2 Lazy Partial Evaluation 93
- 3 Application to Constraint Satisfaction 95
- Acknowledgements and Availability 99
- 4 Discussion 99
- References 99
- 5 Conclusion 99
Chapter 12. A Teaching Method for Reinforcement Learning 102
- Abstract 102
- 1 INTRODUCTION 102
- 3 INTEGRATING THE METHOD 103
- 2 TEACHING METHOD 103
- 4 EXPERIMENT ONE: CART-POLE AND ACE/ASE 104
- 5 EXPERIMENT TWO: RACE TRACK AND Q-LEARNING 107
- 6 CONCLUSIONS 110
- References 111
- Acknowledgments 111
Chapter 13. Compiling Prior Knowledge Into an Explicit Bias 112
- 1 PROBLEMS FACING THEORY-GUIDED LEARNING 112
- Abstract 112
- 3 ANTECEDENT DESCRIPTION GRAMMARS 114
- 2 EXPLICITLY BIASED LEARNING 114
- 4 EXPERIMENTAL RESULTS 117
- 5 CONCLUSIONS 118
- Acknowledgements 119
- A GRENDEL's LEARNING ALGORITHM 119
- References 120
Chapter 14. Spatial analogy and subsumption 121
- 2 Spatio–analogical inference 121
- 1 Introduction 121
- 3 Subsumption and generalization 122
- 4 Retrieval and indexing 123
- 5 Results 125
- 6 Discussion 125
- References 126
- Acknowledgements 126
Chapter 15. Learning to Satisfy Conjunctive Goals 127
- Abstract 127
- 1 Motivation 127
- 2 Opportunism and Learning 128
- 3 An example 129
- 4 When are plans for conjunctive goals necessary? 130
- 5 Reasons for saving plans for conjunctive goals 130
- 6 Stability and Enforcement 131
- 7 Stability and evaluation 131
- 8 Conclusion 132
- Acknowledgements 132
- References 132
Chapter 16. Multistrategy Learning with Introspective Meta-Explanations 133
- Abstract 133
- 1 INTRODUCTION 133
- 2 REPRESENTATION OF INTROSPECTIVE META-XPS 134
- Acknowledgements 137
- References 137
- 3 DISCUSSION 137
Chapter 17. An Asymptotic Analysis of Speedup Learning 139
- Abstract 139
- 1 Introduction 139
- 2 Meta-Level Problem Solvers 139
- 3 Polynomial-time Problem Solving 142
- 4 Macro Problem Solvers 142
- 5 Conclusion 144
- Acknowledgments 145
- References 145
Chapter 18. Why EBL Produces Overly-Specific Knowledge: A Critique of the PRODIGY Approaches 147
- Abstract 147
- 1 Motivation 147
- 2 The Problem of Logical Minimization 148
- 3 Avoiding Overly-Specific Conditions 149
- 4 Related Work 152
- 5 Conclusion 153
- References 153
Chapter 19. Automatic Feature Generation for Problem Solving Systems 154
- Abstract 154
- 1 INTRODUCTION 154
- 2 PROBLEM SOLVING, EVALUATION FUNCTIONS AND FEATURES 155
- 3 A THEORY OF FEATURE GENERATION 155
- 4 THE ZENITH SYSTEM 156
- 5 DOMAINS AND RESULTS 160
- Acknowledgements 163
- References 163
- 6 CONCLUSION 163
Chapter 20. Towards Inductive Generalisation in Higher Order Logic 164
- Abstract 164
- 1 Introduction 164
- 2 Motivation 165
- 3 Mλ: a restricted higher order language 166
- 4 Mλ normal and nonredundant terms 166
- 5 Implementation 167
- 6 Applications 168
- 7 Conclusion and future research directions 169
- Acknowledgements 170
- A Syntax of λ-calculus terms 170
- B Extension to δ0 conversion 171
- References 171
Chapter 21. Ordering Effects in Clustering 173
- Abstract 173
- 1 INTRODUCTION 173
- 2 A REVIEW OF COBWEB 173
- 3 ORDERING EFFECTS 174
- 4 CONTROL STRATEGIES 175
- 5 ORDER INDEPENDENCE 176
- 6 CONCLUDING REMARKS 178
- References 178
Chapter 22. Learning Structured Concepts Using Genetic Algorithms 179
- Abstract 179
- 1 INTRODUCTION 179
- 2 WHY A GENETIC ALGORITHM? 180
- 3 GA-SMART OVERVIEW 180
- 4 MAPPING FORMULAE TO BIT STRINGS 181
- 5 GENETIC ALGORITHM DETAILS 182
- 6. EXPERIMENTATION WITH STANDARD TEST CASES 184
- 7 DISCUSSION 187
- Acknowledgements 187
- References 187
Chapter 23. An Analysis of Learning to Plan as a Search Problem 189
- 1 INTRODUCTION 189
- 2 LEARNING AS SEARCH 189
- Abstract 189
- 3 FRAMEWORK OF SIMPLIFICATIONS 190
- 4 APPLYING THE FRAMEWORK 193
- References 197
- 5 CONCLUSIONS 197
- Acknowledgements 197
Chapter 24. An Approach to Anytime Learning 199
- Abstract 199
- 1 INTRODUCTION 199
- 2 AN ARCHITECTURE FOR ANYTIME LEARNING 200
- 3 A CASE STUDY 201
- 4 SUMMARY 204
- References 205
Chapter 25. Artificial Universes - Towards a Systematic Approach to Evaluating Algorithms which Learn from Examples 206
- 1. INTRODUCTION 206
- 2. MODELLING NOISE IN THE DOMAIN RULES 206
- Abstract 206
- 3. A UNIVERSE: A PROBABILISTIC MODEL OF A DOMAIN 207
- 4. CONSTRUCTING A UNIVERSE - A WORKED EXAMPLE 208
- 5. INFORMATION IN THE UNIVERSE -EVALUATING INDUCED RULES 208
- 6 EXPERIMENTS USING ID3 ON GENERATED EXAMPLES 212
- 7 CONCLUSION AND FUTURE WORK 214
- References 214
Chapter 26. Average Case Analysis of Learning k-CNF Concepts 216
- 2 AN AVERAGE CASE MODEL OF k-CNF 216
- Abstract 216
- 1 INTRODUCTION 216
- 3 IMPLICATIONS OF THE MODEL 219
- 5 CONCLUSION 220
- 4 VIOLATING ASSUMPTIONS 220
- References 221
- Acknowledgements 221
Chapter 27. The MENTLE Approach to Learning Heuristics for the Control of Logic Programs 222
- 1 INTRODUCTION 222
- 2 HEURISTICS FOR GOAL SELECTION 222
- Abstract 222
- 3 MENTLE LEARNING ALGORITHM 223
- 4 CONTRADICTION 226
- 5 RESULTS FROM MENTLE 226
- 6 FURTHER DEVELOPMENT 227
- References 227
Chapter 28. Fuzzy Substructure Discovery 228
- Abstract 228
- 1 INTRODUCTION 228
- 2 SUBSTRUCTURE DISCOVERY 228
- 3 FUZZY GRAPH MATCH 230
- 4 FUZZY SUBSTRUCTURE DISCOVERY 231
- 5 EXAMPLES 232
- 6 CONCLUSIONS 233
- References 233
Chapter 29. Efficient Classification of Massive, Unsegmented Datastreams 234
- Abstract 234
- 1 INTRODUCTION 234
- 2 EXISTING ML APPROACHES FOR FINDING MOTIFS IN DATASTREAMS 235
- 3 OUR APPROACH 235
- 4. TESTING THE CLUSTERER: THE PROTEIN EVOLUTION SIMULATOR 239
- 5. APPLICATION TO REAL DATA 239
- References 241
- 6. CONCLUSIONS 241
Chapter 30. Induction of One-Level Decision Trees 243
- 1 INTRODUCTION 243
- 2 ANALYSIS OF THE ONE-LEVEL ALGORITHM 243
- Abstract 243
- 3 BEHAVIOR OF THE ONE-LEVEL ALGORITHM 246
- 4 DISCUSSION 249
- References 250
- Acknowledgements 250
Chapter 31. Combining Competition and Cooperation in Supervised Inductive Learning 251
- 1 Preliminaries 251
- Abstract 251
- 2 DESIGN 252
- 3 EXPERIMENTS 255
- 4 SUMMARY 257
- References 257
Chapter 32. A Practical Approach to Feature Selection 259
- Abstract 259

Author's affiliation

Peter Edwards: University of Glamorgan

Proceedings of the Ninth International Workshop (ML92)

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