| Foreword | p. V |
| Preface | p. vii |
| Acknowledgments | p. xi |
| The Psychological Basis of Cognitive Modeling | |
| Introduction | p. 1 |
| Cognitive Models of Pattern Recognition | p. 3 |
| Template-Matching Theory | p. 3 |
| Prototype-Matching Theory | p. 4 |
| Feature-Based Approach for Pattern Recognition | p. 4 |
| The Computational Approach | p. 5 |
| Cognitive Models of Memory | p. 6 |
| Atkinson-Shiffrin's Model | p. 6 |
| Debates on Atkinson-Shiffrin's Model | p. 8 |
| Tulving's Model | p. 8 |
| The Parallel Distributed Processing Approach | p. 11 |
| Mental Imagery | |
| Mental Representation of Imagery | p. 12 |
| Rotation of Mental Imagery | p. 12 |
| Imagery and Size | p. 13 |
| Imagery and Shape | p. 14 |
| Part-Whole Relationship in Mental Imagery | p. 14 |
| Ambiguity in Mental Imagery | p. 15 |
| Neurophysiological Similarity between Imagery and Perception | p. 15 |
| Cognitive Maps of Mental Imagery | p. 15 |
| Understanding a Problem | p. 17 |
| Steps in Understanding a Problem | p. 18 |
| A Cybernetic View of Cognition | p. 19 |
| The States of Cognition | p. 20 |
| Computational Modeling of Cognitive Systems | p. 24 |
| Petri Nets: A Brief Review | p. 25 |
| Extension of Petri Net Models for Distributed Modeling of Cognition | p. 29 |
| Scope of the Book | p. 31 |
| Summary | p. 31 |
| Exercises | p. 32 |
| References | p. 35 |
| Parallel and Distributed Logic Programming | |
| Introduction | p. 39 |
| Formal Definitions | p. 41 |
| Preliminary Definitions | p. 41 |
| Properties of the Substitution Set | p. 45 |
| SLD Resolution | p. 47 |
| Concurrency in Resolution | p. 52 |
| Preliminary Definitions | p. 52 |
| Types of Concurrent Resolution | p. 55 |
| Petri Net Model for Concurrent Resolution | p. 61 |
| Extended Petri Net | p. 61 |
| Algorithm for Concurrent Resolution | p. 63 |
| Performance Analysis of Petri Net-Based Models | p. 67 |
| The Speed-up | p. 67 |
| The Resource Utilization Rate | p. 68 |
| Resource Unlimited Speed-up and Utilization Rate | p. 69 |
| Conclusions | p. 70 |
| Exercises | p. 70 |
| References | p. 82 |
| Distributed Reasoning by Fuzzy Petri Nets: A Review | |
| Fuzzy Logic and Approximate Reasoning | p. 85 |
| Structured Models of Approximate Reasoning | p. 87 |
| Looney's Model | p. 89 |
| The Model Proposed by Chen et al | p. 91 |
| Konar and Mandal's Model | p. 93 |
| Yu's Model | p. 97 |
| Chen's Model for Backward Reasoning | p. 100 |
| Bugarin and Barro's Model | p. 102 |
| Pedrycz and Gomide's Learning Model | p. 107 |
| Construction of Reduction Rules Using FPN | p. 110 |
| Scope of Extension of Fuzzy Reasoning on Petri Nets | p. 115 |
| Summary | p. 116 |
| Exercises | p. 117 |
| References | p. 121 |
| Belief Propagation and Belief Revision Models in Fuzzy Petri Nets | |
| Introduction | p. 123 |
| Imprecision Management in an Acyclic FPN | p. 125 |
| Formal Definitions and the Proposed Model | p. 126 |
| Proposed Model for Belief Propagation | p. 126 |
| Proposed Algorithm for Belief Propagation | p. 128 |
| Properties of FPN and Belief Propagation Scheme | p. 132 |
| Imprecision and Inconsistency Management in a Cyclic FPN | p. 134 |
| Proposed Model for Belief Revision | p. 134 |
| Stability Analysis of the Belief Revision Model | p. 135 |
| Detection and Elimination of Limit Cycles | p. 141 |
| Nonmonotonic Reasoning in an FPN | p. 144 |
| Conclusions | p. 147 |
| Exercises | p. 148 |
| References | p. 151 |
| Building Expert Systems Using Fuzzy Petri Nets | |
| Introduction | p. 153 |
| The Database | p. 155 |
| The Data-tree | p. 155 |
| The Knowledge Base | p. 157 |
| The Inference Engine | p. 160 |
| Searching Antecedent Parts of PR in the Data-tree | p. 160 |
| Formation of the FPN | p. 162 |
| Decision Making and Explanation Tracing | p. 163 |
| A Case Study | p. 163 |
| Performance Evaluation | p. 166 |
| Time-Complexisty for the Default-Data-Tree-Formation Procedure | p. 166 |
| Time-Complexity for the Procedure Suspect-Identification | p. 167 |
| Time-Complexity for the Procedure Variable-Instantiation-of-PRs | p. 168 |
| Time-Complexity for the Procedure Create-tree | p. 169 |
| Time-Complexity for the Procedure Search-on-Data-Tree | p. 170 |
| Time-Complexity for the Procedure FPN-Formation | p. 171 |
| Time-Complexity for the Belief-Revision and Limit-Cycle-Detection Procedure | p. 173 |
| Time-Complexity Analysis for the Procedure Limit-Cycle-Elimination | p. 174 |
| Time-Complexity for the Procedure Nonmonotonic Reasoning | p. 175 |
| Time-Complexity for the Procedure Decision-Making and Explanation Tracing | p. 176 |
| Time-Complexity of the Overall Expert System | p. 177 |
| Conclusions | p. 178 |
| Exercises | p. 178 |
| References | p. 179 |
| Distributed Learning Using Fuzzy Cognitive Maps | |
| Introduction | p. 181 |
| Axelord's Cognitive Maps | p. 182 |
| Kosko's Model | p. 184 |
| Kosko's Extended Model | p. 187 |
| Adaptive FCMs | p. 188 |
| Zhang, Chen, and Bezdek's Model | p. 189 |
| Pal and Konar's FCM Model | p. 191 |
| Conclusions | p. 197 |
| Exercises | p. 197 |
| References | p. 201 |
| Unsupervised Learning by Fuzzy Petri Nets | |
| Introduction | p. 205 |
| The Proposed Model for Cognitive Learning | p. 206 |
| Encoding of Weights | p. 208 |
| The Recall Model | p. 208 |
| State-Space Formulation | p. 210 |
| State-Space Model for Belief Updating | p. 211 |
| State-Space Model for FTT Updating of Transitions | p. 211 |
| State-Space Model for Weights | p. 212 |
| Stability Analysis of the Cognitive Model | p. 212 |
| Computer Simulation | p. 216 |
| Implication of the Results | p. 219 |
| Knowledge Refinement by Hebbian Learning | p. 219 |
| The Encoding Model | p. 219 |
| The Recall/Reasoning Model | p. 221 |
| Case Study by Computer Simulation | p. 221 |
| Implication of the Results | p. 226 |
| Conclusions | p. 226 |
| Exercises | p. 228 |
| References | p. 229 |
| Supervised Learning by a Fuzzy Petri Net | |
| Introduction | p. 233 |
| Proposed Model of Fuzzy Petri Nets | p. 234 |
| State-Space Formulation | p. 236 |
| Algorithm for Training | p. 238 |
| Analysis of Convergence | p. 243 |
| Application in Fuzzy Pattern Recognition | p. 245 |
| Conclusions | p. 252 |
| Exercises | p. 252 |
| References | p. 253 |
| Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets | |
| Introduction | p. 257 |
| Formal Definitions | p. 259 |
| State-Space Formulation of the Proposed FPN Model | p. 262 |
| The Behavioral Model of FPN | p. 263 |
| State-Space Formulation of the Model | p. 265 |
| Special Cases of the Model | p. 266 |
| Stability Analysis | p. 269 |
| Forward Reasoning in FPNs | p. 272 |
| Abductive Reasoning in FPN | p. 274 |
| Bi-directional Reasoning in an FPN | p. 279 |
| Fuzzy Modus Tollens and Duality | p. 288 |
| Conclusions | p. 291 |
| Exercises | p. 292 |
| References | p. 293 |
| Human Mood Detection and Control: A Cybernetic Approach | |
| Introduction | p. 297 |
| Filtering, Segmentation and Localization of Facial Components | p. 299 |
| Segmentation of the Mouth Region | p. 300 |
| Segmentation of the Eye Region | p. 302 |
| Segmentation of Eyebrow Constriction | p. 303 |
| Determination of Facial Attributes | p. 303 |
| Determination of the Mouth-Opening | p. 303 |
| Determination of the Eye-Opening | p. 304 |
| Determination of the Length of Eyebrow-Constriction | p. 305 |
| Fuzzy Relational Model for Mood Detection | p. 306 |
| Fuzzification of Facial Attributes | p. 306 |
| The Fuzzy Relational Model for Mood Detection | p. 307 |
| Validation of System Performance | p. 309 |
| A Basic Scheme of Human Mood Control | p. 310 |
| A Simple Model of Human Mood Transition Dynamics | p. 311 |
| The Model | p. 312 |
| Properties of the Model | p. 315 |
| The Proportional Model of Human Mood Control | p. 317 |
| Mamdani's Model for Mood Control | p. 318 |
| Ranking the Music, Audio, and Video Clips | p. 326 |
| Experimental Results | p. 327 |
| Conclusions | p. 328 |
| Exercises | p. 328 |
| References | p. 329 |
| Distributed Planning and Multi-agent Coordination of Robots | |
| Introduction | p. 331 |
| Single-Agent Planning | p. 333 |
| Multi-Agent Planning | p. 336 |
| Task Sharing and Distribution in Multi-agent Planning | p. 336 |
| Cooperation with/without Communication | p. 336 |
| Homogeneous and Heterogeneous Distributed Planning | p. 338 |
| Vision-based Transportation of Blocks by Two Robots | p. 339 |
| Experimental Results | p. 341 |
| Timing Analysis of the Transportation Problem | p. 343 |
| Analysis with Two agents | p. 343 |
| Analysis with /-agents | p. 345 |
| Conclusions | p. 347 |
| Exercises | p. 347 |
| References | p. 348 |
| Index | p. 351 |
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