Cognitive Engineering: A Distributed Approach to Machine Intelligence

✍ Scribed by Amit Konar

• Publisher: Springer
• Year: 2005
• Tongue: English
• Leaves: 366
• Series: Advanced Information and Knowledge Processing
• Edition: 1st Edition.
• Category: Library

✦ Synopsis

This book explores the design issues of intelligent engineering systems. Beginning with the foundations of psychological modeling of the human mind, the main emphasis is given to parallel & distributed realization of intelligent models for application in reasoning, learning, planning & multi-agent co-ordination problems. Case studies on human-mood detection & control, & behavioral co-operation of mobile robots are provided. This is the 1st comprehensive text of its kind, bridging the gap between Cognitive Science & Cognitive Systems Engineering. Each chapter includes plenty of numerical examples & exercises with sufficient hints, so that readers can solve the exercises on their own. Computer simulations are included in most chapters to give a clear idea about the application of the algorithms undertaken. This book is unique in its theme & contents - written with graduates in mind, it would also be a valuable resource for researchers in the fields Cognitive Science, Computer Science & Cognitive Engineering.

✦ Table of Contents

Cognitive Engineering. A Distributed Approach to Machine Intelligence......Page 3
1.1 Introduction......Page 17
1.2.1 Template-Matching Theory......Page 19
1.2.3 Feature-Based Approach for Pattern Recognition......Page 20
1.2.4 The Computational Approach......Page 21
1.3.1 Atkinson-Shiffrin’s Model......Page 22
1.3.3 Tulving’s Model......Page 24
1.3.4 The Parallel Distributed Processing Approach......Page 27
1.4.2 Rotation of Mental Imagery......Page 28
1.4.3 Imagery and Size......Page 29
1.4.5 Part-Whole Relationship in Mental Imagery......Page 30
1.4.8 Cognitive Maps of Mental Imagery......Page 31
1.5 Understanding a Problem......Page 33
1.5.1 Steps in Understanding a Problem......Page 34
1.6 A Cybernetic View of Cognition......Page 35
1.6.1 The States of Cognition......Page 36
1.7 Computational Modeling of Cognitive Systems......Page 40
1.8 Petri Nets: A Brief Review......Page 41
1.9 Extension of Petri Net Models for Distributed Modeling of Cognition......Page 45
1.11 Summary......Page 47
Exercises......Page 48
References......Page 51
2.1 Introduction......Page 55
2.2.1 Preliminary Definitions......Page 57
2.2.2 Properties of the Substitution Set......Page 61
2.2.3 SLD Resolution......Page 63
2.3.1 Preliminary Definitions......Page 68
2.3.2 Types of Concurrent Resolution......Page 71
2.4.1 Extended Petri Net......Page 77
2.4.2 Algorithm for Concurrent Resolution......Page 79
2.5.1 The Speed-up......Page 83
2.5.2 The Resource Utilization Rate......Page 84
2.5.3 Resource Unlimited Speed-up and Utilization Rate......Page 85
2.6 Conclusions......Page 86
References......Page 98
3.1 Fuzzy Logic and Approximate Reasoning......Page 101
3.2 Structured Models of Approximate Reasoning......Page 103
3.3 Looney’s Model......Page 105
3.4 The Model Proposed by Chen et al.......Page 107
3.5 Konar and Mandal’s Model......Page 109
3.6 Yu’s Model......Page 113
3.7 Chen’s Model for Backward Reasoning......Page 116
3.8 Bugarin and Barro’s Model......Page 118
3.9 Pedrycz and Gomide’s Learning Model......Page 123
3.10 Construction of Reduction Rules Using FPN......Page 126
3.11 Scope of Extension of Fuzzy Reasoning on Petri Nets......Page 131
3.12 Summary......Page 132
Exercises......Page 133
References......Page 137
4.1 Introduction......Page 139
4.2.1 Formal Definitions and the Proposed Model......Page 141
4.2.2 Proposed Model for Belief Propagation......Page 142
4.2.3 Proposed Algorithm for Belief Propagation......Page 144
4.2.4 Properties of FPN and Belief Propagation Scheme......Page 148
4.3.1 Proposed Model for Belief-Revision......Page 150
4.3.2 Stability Analysis of the Belief Revision Model......Page 151
4.3.3 Detection and Elimination of Limit Cycles......Page 157
4.3.4 Nonmonotonic Reasoning in an FPN......Page 160
4.4 Conclusions......Page 163
Exercises......Page 164
References......Page 167
5.1 Introduction......Page 169
5.2.1 The Data-tree......Page 171
5.3 The Knowledge Base......Page 173
5.4.1 Searching Antecedent Parts of PR in the Data-tree......Page 176
5.4.2 Formation of the FPN......Page 178
5.5 A Case Study......Page 179
5.6.1 Time-Complexity for the Default-Data-Tree-Formation Procedure......Page 182
5.6.2 Time-Complexity for the Procedure Suspect-Identification......Page 183
5.6.3 Time-Complexity for the Procedure Variable-Instantiation-of-PRs......Page 184
5.6.4 Time-Complexity for the Procedure Create-tree......Page 185
5.6.5 Time-Complexity for the Procedure Search-on-Data-Tree......Page 186
5.6.6 Time-Complexity for the Procedure FPN-Formation......Page 187
5.6.7 Time-Complexity for the Belief-Revision and Limit-Cycle-Detection Procedure......Page 189
5.6.8
Time-Complexity Analysis for the Procedure Limit-Cycle-Elimination......Page 190
5.6.9 Time-Complexity for the Procedure Nonmonotonic Reasoning......Page 191
5.6.10 Time-Complexity for the Procedure Decision-Making and Explanation-Tracing......Page 192
5.6.11 Time-Complexity of the Overall Expert System......Page 193
Exercises......Page 194
References......Page 195
6.1 Introduction......Page 197
6.2 Axelrod’s Cognitive Maps......Page 198
6.3 Kosko’s Model......Page 200
6.4 Kosko’s Extended Model......Page 203
6.5 Adaptive FCMs......Page 204
6.6 Zhang, Chen, and Bezdek’s Model......Page 205
6.7 Pal and Konar’s FCM Model......Page 207
Exercises......Page 213
References......Page 217
7.1 Introduction......Page 221
7.2 The Proposed Model for Cognitive Learning......Page 222
7.2.2 The Recall Model......Page 224
7.3 State-Space Formulation......Page 226
7.3.2 State-Space Model for FTT Updating of Transitions......Page 227
7.4 Stability Analysis of the Cognitive Model......Page 228
7.5 Computer Simulation......Page 232
7.7.1 The Encoding Model......Page 235
7.7.3 Case Study by Computer Simulation......Page 237
7.8 Conclusions......Page 242
Exercises......Page 244
References......Page 245
8.1 Introduction......Page 248
8.2 Proposed Model of Fuzzy Petri Nets......Page 249
8.2.1 State-Space Formulation......Page 251
8.3 Algorithm for Training......Page 253
8.4 Analysis of Convergence......Page 258
8.5 Application in Fuzzy Pattern Recognition......Page 260
Exercises......Page 267
References......Page 268
9.1 Introduction......Page 271
9.2 Formal Definitions......Page 273
9.3 State-Space Formulation of the ProposedFPN Model......Page 276
9.3.1 The Behavioral Model of FPN......Page 277
9.3.2 State-Space Formulation of the Model......Page 279
9.3.3 Special Cases of the Model......Page 280
9.4 Stability Analysis......Page 283
9.5 Forward Reasoning in FPN......Page 286
9.6 Abductive Reasoning in FPN......Page 288
9.7 Bi-directional Reasoning in an FPN......Page 293
9.8 Fuzzy Modus Tollens and Duality......Page 302
9.9 Conclusions......Page 305
Exercises......Page 306
References......Page 307
10.1 Introduction......Page 311
10.2 Filtering, Segmentation, and Localization of Facial Components......Page 313
10.2.1 Segmentation of the Mouth Region......Page 314
10.2.2 Segmentation of the Eye Region......Page 316
10.3.1 Determination of the Mouth-Opening......Page 317
10.3.2 Determination of the Eye-Opening......Page 318
10.3.3 Determination of the Length of Eyebrow Constriction......Page 319
10.4.1 Fuzzification of Facial Attributes......Page 320
10.4.2 The Fuzzy Relational Model for Mood Detection......Page 321
10.5 Validation of System Performance......Page 323
10.6 A Basic Scheme of Human Mood Control......Page 324
10.7 A Simple Model of Human Mood TransitionDynamics......Page 325
10.7.1 The Model......Page 326
10.7.2 Properties of the Model......Page 329
10.8 The Proportional Model of Human MoodControl......Page 331
10.9 Mamdani’s Model for Mood Control......Page 333
10.10 Ranking the Music, Audio, and Video Clips......Page 340
10.11 Experimental Results......Page 341
Exercises......Page 342
References......Page 343
11.1 Introduction......Page 345
11.2 Single-Agent Planning......Page 347
11.3.2 Cooperation with/without Communication......Page 350
11.3.3 Homogeneous and Heterogeneous Distributed Planning......Page 352
11.4 Vision-Based Transportation of Blocks by Two Robots......Page 353
11.5 Experimental Results......Page 355
11.6.1 Analysis with Two Agents......Page 357
11.6.2 Analysis with l Agents......Page 359
Exercises......Page 361
References......Page 362
Index......Page 364