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Informationen zum Autor David B. Fogel, PhD, is the Chief Executive Officer of Natural Selection Inc. and was the general chairman of the 2002 IEEE World Congress on Computational Intelligence. Charles J. Robinson, DSc, PE, is Director of the University Center for Biomedical Engineering and Rehabilitation Science at Louisiana Tech University and a Senior Rehabilitation Research Career Scientist with the Shreveport, LA, Veterans Affairs Medical Center. He served as plenary chairman of the 2002 IEEE World Congress on Computational Intelligence. Klappentext * Computational intelligence is the term used by computer scientists to describe the overlapping technical interest areas that would be more readily identified by nontechnologists as AI* Includes past, present, and future examples of applications developed from computational intelligence studies* The editors and contributors are the leaders in the CI community writing in their respective fields of expertise Zusammenfassung * Computational intelligence is the term used by computer scientists to describe the overlapping technical interest areas that would be more readily identified by nontechnologists as AI* Includes past, present, and future examples of applications developed from computational intelligence studies* The editors and contributors are the leaders in the CI community writing in their respective fields of expertise Inhaltsverzeichnis CONTRIBUTORS. PREFACE. 1. THREE GENERATIONS OF COEVOLUTIONARY ROBOTICS (Jordan B. Pollack, Hod Lipson, Pablo Funes, and Gregory 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. Wer...
