Computational Intelligence: The Experts Speak

The definitive survey of computational intelligence from luminaries in the field
Computational intelligence is a fast–moving, multidisciplinary field–the nexus of diverse technical interest areas that include neural networks, fuzzy logic, and evolutionary computation. Keeping up with computational intelligence means understanding how it relates to an ever–expanding range of applications. This is the book that ties it all together–and puts that understanding well within your reach.
In Computational Intelligence: The Experts Speak, editors David B. Fogel and Charles J. Robinson present an unmatched compilation of expanded papers from plenary and special lecturers attending the 2002 IEEE World Congress on Computational Intelligence. Collectively, these papers provide a compelling snapshot of the issues that define the industry, as observed by some of the top minds in the computational intelligence community. In a series of topical chapters, this comprehensive volume shows how current technology is shaping computational intelligence, and it delivers eye–opening insights into the field’s future challenges.
The research detailed here covers an array of leading–edge applications, from coevolutionary robotics to underwater sensors and cognitive science, in such areas as:Self–organizing systemsSituation awarenessHuman–machine interactionAutomatic controlData recognition
Computational Intelligence also includes introductions to each grouping of contributions that provide helpful tutorials and discuss important parallels between topics.
Whatever your role might be in this dynamic, influential field, this is the one reference that no practitioner of computational intelligence should be without.

Spis treści:
CONTRIBUTORS.
PREFACE.
1. THREE GENERATIONS OF COEVOLUTIONARY ROBOTICS (Jordan B. Pollack, Hod Lipson, Pablo Funes, and Grego ry Hornby).
1.1 Roboeconomics.
1.2 Coevolution.
1.3 Research Thrusts.
1.4 Conclusion.
Acknowledgments.
References.
2. BEYOND 2001: THE LINGUISTIC SPATIAL ODYSSEY (James M. Keller, Pascal Matsakis, and Marjorie Skubic).
2.1 Introduction.
2.2 Force Histograms and Linguistic Scene Description.
2.3 Scene Matching.
2.4 Human–Robot Dialog.
2.5 Sketched Route Map Understanding.
2.6 The Future.
Acknowledgments.
References.
3. COMPUTING MACHINERY AND INTELLIGENCE AMPLIFICATION (Steven K. Rogers, Matthew Kabrisky, Kenneth Bauer, and Mark E. Oxley).
3.1 Introduction.
3.2 Estimating Intelligence.
3.3 Turing Test and Intelligence Amplification.
3.4 Measuring Intelligence Amplification.
3.5 The Future of Intelligence Amplification.
References.
4. VISUALIZING COMPLEXITY IN THE BRAIN (Lloyd Watts).
4.1 Introduction.
4.2 Neuroscience Knowledge.
4.3 Computing Technology.
4.4 Nontechnical Issues.
4.5 Conclusions.
References.
5. EMERGING TECHNOLOGIES: ONR’S NEED FOR INTELLIGENT COMPUTATION IN UNDERWATER SENSORS (James F. McEachern and Robert T. Miyamoto).
5.1 Introduction.
5.2 Background.
5.3 The Challenge.
5.4 Current Applications.
5.5 Future.
5.6 Summary.
References.
6. BEYOND VOLTERRA AND WIENER: OPTIMAL MODELING OF NONLINEAR DYNAMICAL SYSTEMS IN A NEURAL SPACE FOR APPLICATIONS IN COMPUTATIONAL INTELLIGENCE (Rui J. P. de Figueiredo).
6.1 Introduction.
6.2 Classes of Nonlinear Dynamical System Models.
6.3 The de Figueiredo–Dwyer–Zyla Space F.
6.4 Derivation of Sigmoid Functionals.
6.5 Best Robust Approximation of f in the Neural Space N.
6.6 Optimal Combined Structural and Parametric Modeling of Nonlinear Dynamical Systems in N.
6.7 Computationally Intelligent (CI) Systems.
6.8 Concluding Remarks.
References.
7. TECHNIQUES FOR EXTRACTING CLASSIFICATION AND REGRESSION RULES FROM ARTIFICIAL NEURAL NETWORKS (Rudy Setiono ).
7.1 Introduction.
7.2 Rule Extraction.
7.3 Illustrative Examples.
7.4 Conclusion.
References.
8. NEURAL NETWORKS FOR CONTROL: RESEARCH OPPORTUNITIES AND RECENT DEVELOPMENTS (Paul J. Werbos).
8.1 The Challenge to Researchers: Context and Motivation.
8.2 A Specific Challenge and Associated Issues.
8.3 Strategies, Tasks, and Tools.
References.
9. INTELLIGENT LEARNING ROBOTIC SYSTEMS USING COMPUTATIONAL INTELLIGENCE (Toshio Fukuda and Naoyuki Kubota).
9.1 Introduction.
9.2 Motion Planning and Behavior Acquisition of Robots.
9.3 Emerging Synthesis of Computational Intelligence.
9.4 Intelligence on Robotic Systems.
9.5 Structured Intelligence for Robotic Systems.
9.6 Concluding Remarks.
References.
10. COMPUTATIONAL INTELLIGENCE IN LOGISTICS (Hans–Jürgen Zimmermann).
10.1 Introduction.
10.2 Traffic Management.
10.3 Fleet Management.
10.4 In–House Logistics.
10.5 Conclusions.
References.
11. TWO NEW CONVERGENCE RESULTS FOR ALTERNATING OPTIMIZATION (James C. Bezdek and Richard J. Hathaway).
11.1 Iterative Optimization.
11.2 Existence and Uniqueness.
11.3 The Alternating Optimization Algorithm.
11.4 When Is Alternating Optimization a Good Choice?
11.5 How Do We Solve (11.1)?
11.6 Local Convergence of Alternating Optimization.
11.7 Global Convergence of AO.
11.8 Conclusions.
Acknowledgment.
References.
12. CONSTRUCTIVE DESIGN OF A DISCRETE–TIME FUZZY CONTROLLER BASED ON PIECEWISE–LYAPUNOV FUNCTIONS (Gang Feng, Dong Sun, and Louis Wang).
12.1 Introduction.
12.2 Fuzzy Dynamic Model and Its Piecewise–Quadratic Stability.
12.3 Controller Synthesis of Fuzzy Dynamic Systems.
12.4 Simulation Examples.
12.5 Conclusions.
Acknowledgments.
References.
Appendix.
13. EVOLUTIONARY COMPUTATION AND COGNITIVE SCIENCE (Janet Wiles and Jennifer Hallinan).
13.1 Cognitive Science: What’s on Your Mind?
1 3.2 Case Studies in Evolutionary Computation and Cognitive Science.
13.3 Summary.
References.
14. EVOLVABLE HARDWARE AND ITS APPLICATIONS (T. Higuchi, E. Takahashi, Y. Kasai, T. Itatani, M. Iwata, H. Sakanashi, M. Murakawa, I. Kajitani, and H. Nosato).
14.1 Introduction.
14.2 Myoelectric Prosthetic Hand Controller with EHW.
14.3 Data–Compression Chip for Printing Image Data.
14.4 Analog EHW Chip for Cellular Phone.
14.5 An EHW–Based Clock–Timing Adjusting Chip.
14.6 Evolvable Optical Systems and Their Application.
14.7 Current Research on EHW.
References.
15. HUMANIZED COMPUTATIONAL INTELLIGENCE WITH INTERACTIVE EVOLUTIONARY COMPUTATION (Hideyuki Takagi).
15.1 Introduction.
15.2 Humanized Computational Intelligence.
15.3 Interactive Evolutionary Computation.
15.4 Conclusion.
References.
16. UNSUPERVISED LEARNING BY ARTIFICIAL NEURAL NETWORKS (Harold Szu).
16.1 A New Challenge: Space–Variant Unsupervised Classifications.
16.2 Power of Pairs: Vector versus Scalar Data.
16.3 Generalization of Shannon’s Entropy Information Theory to Open Systems.
16.4 Benchmarks of Space–Variant Unsupervised Classification.
16.5 Multispectral Medical Imaging.
16.6 Multispectral Remote Sensing.
16.7 Biological Relevance.
16.8 Conclusion.
Acknowledgments.
References.
17. COLLECTIVE INTELLIGENCE (David H. Wolpert).
17.1 Motivation and Background.
17.2 The Mathematics of Designing Collectives.
17.3 Tests of the Mathematics.
17.4 Conclusion.
References.
18. BACKPROPAGATION: GENERAL PRINCIPLES AND ISSUES FOR BIOLOGY (Paul J. Werbos).
18.1 Introduction.
18.2 The Chain Rule for Ordered Derivatives.
18.3 Backpropagation for Supervised Learning.
18.4 Discussion and Future Research.
References.
INDEX.
ABOUT THE EDITORS.

Nota biograficzna:
David B. Fogel, PhD, is the Chief Executive Officer of Natural Selection Inc. and w