Among the first uses of the computer was the development of programs to model perception, reasoning, learning, and evolution. Further developments resulted in computers and programs that exhibit aspects of intelligent behavior. The field of artificial intelligence is based on the premise that thought processes can be computationally modeled. Computational molecular biology brought a similar approach to the study of living systems. In both cases, hypotheses concerning the structure, function, and evolution of cognitive systems (natural as well as synthetic) take the form of computer programs that store, organize, manipulate, and use information.Systems whose information processing structures are fully programmed are difficult to design for all but the simplest applications. Real-world environments call for systems that are able to modify their behavior by changing their information processing structures. Cognitive and information structures and processes, embodied in living systems, display many effective designs for biological intelligent agents. They are also a source of ideas for designing artificial intelligent agents. This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning.The book is organized around four topics: the power of evolution to determine effective solutions to complex tasks, mechanisms to make evolutionary design scalable, the use of evolutionary search in conjunction with local learning algorithms, and the extension of evolutionary search in novel directions.
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Book Description
Editorial Reviews
Review
"This book collects papers on the most interesting ongoing research on evolutionary approaches to the development of artificial neural networks. It will prove extremely valuable for researchers and educators in academia as well as in industry."--Marco Dorigo, FNRS (Fonds National de la Recherche Scientifique) Senior Research Associate, IRIDIA (Institut de Recherches Interdisciplinaires et de Developpements en Intelligence Artificielle), Universite Libre de BruxellesPlease note: Acute accents appear over the first "e" in "Developpements" and over the last "e" in "Universite."
About the Author
This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning.
Vasant Honavar is Associate Professor of Computer Science, Bioinformatics and Computational Biology, and Neuroscience and Director of the Artificial Intelligence Research Laboratory at Iowa State University.
Karthik Balakrishnan leads the Decision Analytics Group at Obongo Inc., California.
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7 of 8 people found the following review helpful:
5.0 out of 5 stars
A welcome addition to the literature on Intelligent Agents!,
By A Customer
This review is from: Advances in the Evolutionary Synthesis of Intelligent Agents (Hardcover)
This book is an excellent reference book on evolutionary design of intelligent agents. It is a welcome addition to the growing body of literature on intelligent agents in general, and evolutionary approaches to synthesis of such agents in particular. One of the editors, Professor Honavar, as amply demonstrated by his other publications, has the rare ability to interrelate an amazing diversity of perspectives on Artificial Intelligence - from symbolic methods to neural networks and evolutionary apporaches, to uncover the shared principles and common foundations, and show how different paradigms can be brought together in synergistic ways to advance our ability to design and analyze intelligent systems. This book includes chapters written by some of the leading experts in the field. The first chapter provides an excellent introduction to the topic. The rest of the chapters provide a veritable feast of material ranging from design of sensory systems for robots to current research on design of software agents. Most of the chapters, although they describe some of the latest research in this area, are written in a style that is accessible to even an interested layperson. (My 15 year old daughter has enjoyed reading several chapters). The book should be especially useful as a reference to graduate students and researchers interested in artificial intelligence, machine learning, evolutionary computation, software agents, and computational evolutionary biology. It should also be useful as supplementary reading for a graduate or upper level undergraduate course on evolutionary computation, computational intelligence, or a related area or as a primary text for a seminar course. I recommend this book enthusiastically to everyone who is interested in the science and engineering of intelligent agents.
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Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Inside This Book
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First Sentence:
From its very inception in the late 1950s and early 1960s, the field of Artificial Intelligence, has concerned itself with the analysis and synthesis of intelligent agents. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
output coding strategy, quadripod gait, external input sensors, output pointer cell, ontogenetic adaptive changes, genotypic multiplicity, two child cells, phenotypic redundancy, local selection algorithm, last emitter, developmental genomes, artificial metabolism, sparse reinforcement, sequential decision tasks, neural agents, offspring network, basis function centers, testing error rate, synthetic evolution, options heuristic, graph generation system, leg controller, evolutionary artificial neural networks, recurrent links, reproductive immaturity Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Morgan Kaufmann, Simulation of Adaptive Behavior, San Mateo, Department of Computer Science, Complex Systems, San Diego, Springer Verlag, Addison Wesley, The Netherlands, Heart Mean, Int'l Conf, Iowa State University, Leiden University, Olivier Michel, Parallel Problem Solving, Academic Press, Diabetes Mean, Proceedings of the Third European Conference, Redwood City, Generation Figure, Master Tournament, Press-Bradford Books, Santa Fe Institute Studies, Sciences of Complexity New!
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Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
From its very inception in the late 1950s and early 1960s, the field of Artificial Intelligence, has concerned itself with the analysis and synthesis of intelligent agents. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
output coding strategy, quadripod gait, external input sensors, output pointer cell, ontogenetic adaptive changes, genotypic multiplicity, two child cells, phenotypic redundancy, local selection algorithm, last emitter, developmental genomes, artificial metabolism, sparse reinforcement, sequential decision tasks, neural agents, offspring network, basis function centers, testing error rate, synthetic evolution, options heuristic, graph generation system, leg controller, evolutionary artificial neural networks, recurrent links, reproductive immaturity Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Morgan Kaufmann, Simulation of Adaptive Behavior, San Mateo, Department of Computer Science, Complex Systems, San Diego, Springer Verlag, Addison Wesley, The Netherlands, Heart Mean, Int'l Conf, Iowa State University, Leiden University, Olivier Michel, Parallel Problem Solving, Academic Press, Diabetes Mean, Proceedings of the Third European Conference, Redwood City, Generation Figure, Master Tournament, Press-Bradford Books, Santa Fe Institute Studies, Sciences of Complexity New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Citations (learn more)
This book cites 84
books:
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4
books
cite this book:
- Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg on 4 pages
- Advances in Neural Information Processing Systems 12 by Todd K. Leen on 4 pages
- Advances in Genetic Programming (Complex Adaptive Systems) by Kenneth E. Kinnear Jr. on page 24, page 26, and page 152
- Introduction To The Theory Of Neural Computation, Volume I (Santa Fe Institute Series) by John A. Hertz on page 24, page 182, and page 212
- Neural Networks Hb (UNICOM - Information & Communications Technology) by J G Taylor on page 89, page 304, and page 425
See all 84 books this book cites
- Embodied Artificial Intelligence: International Seminar, Dagstuhl Castle, Germany, July 7-11, 2003, Revised Selected Papers (Lecture Notes in Computer ... / Lecture Notes in Artificial Intelligence) by Fumiya Iida on page 310
- Trends and Applications in Constructive Approximation (International Series of Numerical Mathematics) by Detlef H. Mache on page 122
- Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between ... Computer Science and General Issues) by José Mira on page 384
- Information Processing with Evolutionary Algorithms: From Industrial Applications to Academic Speculations (Advanced Information and Knowledge Processing) by Manuel Grana on page 127
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