zeiden@ai.cs.wisc.edu (Matthew Zeidenberg) (12/06/89)
My book "Neural Networks in Artificial Intelligence" will be published shortly by Ellis Horwood Ltd., Chichester UK. I am posting the table of contents and the bibliography (in refer format). Chapter 1 Issues in Neural Network Modeling 15 1.1. Introduction 15 1.2. The Statistical Nature of Connectionist Models 17 1.3. Relevance of the Brain 19 1.4. Distributed vs. Local Connectionism 20 1.5. Distributed Models: A Critique 26 1.6. Connectionist Models and the Fuzzy Propositional Approach 27 1.7. Philosophical Issues 29 1.8. Smolensky's "Proper Treatment" of Connectionism 29 1.9. Connectionism: A New Form of Associationism? 35 Chapter 2 Neural Network Methods for Learning and Relaxation 41 2.1. Introduction 41 2.2. Types of Model Neurons 46 2.3. Types of Activation Rules 48 2.4. Early Learning Models 49 2.5. Hebbian and Associative Learning 51 2.6. Kohonen's Work on Associative Learning 54 2.7. Willshaw's Binary Associator 56 2.8. Hopfield's Non-linear Auto-associator 56 2.9. Modeling Neurons with Differential Equations 60 2.10. Simulated Annealing in the Boltzmann Machine 62 2.11. Learning Weights in the Boltzmann Machine 64 2.12. Error Back-Propagation 67 2.13. Applications of Back-propagation 70 2.14. Learning Family Relationships 71 2.15. Competitive Learning 73 2.16. Competitive Learning using Feed-forward Networks 73 2.17. Competitive Learning using Adaptive Resonance Theory 78 2.18. Kohonen's Self-organizing Topological Maps 82 2.19. A Population Biology Approach to Connectionism 86 2.20. Genetic Algorithms 91 2.21. Reinforcement Algorithms 94 2.22. Temporal Difference Methods 98 2.23. Problem-Solving Using Reinforcement and Back-propagation 2.24. Problem-Solving Networks 107 2.25. Extensions to Learning Algorithms 111 2.26. Escaping From Local Minima 112 2.27. Creating Bottlenecks 113 2.28. Sequential Learning 115 2.29. Remembering Old Knowledge 117 2.30. Sequential Processing 120 2.31. Image Compression Using a Back-propagation Auto-associator 122 2.32. Representing Recursive Structures 123 Chapter 3 Production Systems and Expert Systems 127 3.1. Introduction 127 3.2. A Connectionist Production System 128 3.3. Saito and Nakano's Connectionist Expert System 131 3.4. Gallant's Connectionist Expert System 134 Chapter 4 Knowledge Representation 138 4.1. Introduction 138 4.2. Storing Schemata in Neural Networks 139 4.3. Storing Frames in Neural Networks 140 4.4. Storing Schemata with a Complex Neural Architecture 144 4.5. Learning Microfeatures for Knowledge Representation 148 4.6. Implementing Evidential Reasoning and Inheritance Hierarchies 151 Chapter 5 Speech Recognition and Synthesis 157 5.1. Introduction 157 5.2. Comparing Algorithms for Speech Recognition 158 5.3. Speech Recognition as Sequence Comparison 160 5.4. The Temporal Flow Model 163 5.5. The TRACE model 165 5.6. A Model of the Print-to-speech Transformation Process 168 5.7. NETtalk: Reading Aloud with a Three-Layer Perceptron 172 Chapter 6 Visual Perception and Pattern Recognition 177 6.1. Introduction 177 6.2. Interpreting Origami Figures 178 6.3. Recognition Cones 183 6.4. Separating Figure from Ground 185 6.5. Determining "What" and "Where" in a Visual Scene 188 6.6. Linking Visual and Verbal Semantics 192 6.7. Recognizing Image-schemas 193 Chapter 7 Language Understanding 195 7.1. Introduction 195 7.2. Processing Finite State Grammars Sequentially 200 7.3. Sentence Interpretation 205 7.4. Word Sense Disambiguation 210 7.5. Making Case Role Assignments 212 7.6. The MPNP Parsing System 215 7.7. Parsing Strings from Context-Free Grammars 218 7.8. PARSNIP: A Parsing System Based on Back-propagation 221 7.9. A Quasi-Context-Free Parsing System 223 7.10. Parsing Using a Boltzmann Machine 225 7.11. Learning the Past Tense 227 7.12. A Critique of "Learning the Past Tense" 230 7.13. Letter and Word Recognition 232 %A Jr. A. J. 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