Neural Networks and Intellect: Using Model-Based Concepts

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Neural Networks and Intellect: Using Model-Based Concepts describes a new mathematical concept of modeling field theory and its applications to a variety of problems. Examining the relationships among mathematics, computations in neural networks, signs and symbols in semiotics, and ideas of mind in psychology and philosophy, this unique text discusses deep philosophical questions in detail and relates them to mathematics and the engineering of intelligence. Ideal for courses in neural networks, modern pattern recognition, and mathematical concepts of intelligence, it will also be of interest to anyone working in a variety of fields including neural networks, AI, cognitive science, fuzzy systems, pattern recognition and machine/computer vision, data mining, robotics, target tracking, and financial forecasting. Neural Networks and Intellect describes model-based neural networks that utilize the intriguing concept of an internal "world" model, an idea that originated in artificial intelligence and cognitive psychology but whose roots date back to Plato and Aristotle. Combining the a priori knowledge with adaptive learning, the new mathematical concept addresses the most perplexing problems in the field of neural networks: fast learning and robust generalization. The author provides an overview of computational intelligence and neural networks, relating hundreds of seemingly disparate techniques to several fundamental mathematical concepts, which are in turn linked to concepts of mind in philosophy, psychology, and linguistics. Topics covered include the hierarchical and heterarchical organization of intelligent systems, statistical learning theory, genetic algorithms, complex adaptive systems, mathematical semiotics, the dynamic nature of symbols, Godel theorems and intelligence, emotions and thinking, the mathematics of emotional intellect, consciousness, and more. Perlovsky's remarkable conclusion is that the work of ancient philosophers came closer to the computational concepts emerging today than that of pattern recognition and AI experts of just a few years ago. The following website contains information about Dr. Perlovsky's current research related to the theory developed in the book and about available funding opportunities under a Research Associateship Program: to find it search for Perlovsky on http://www4/nationalacademies.org/pga/rap.nsf. Other sources of funding might be available for US-based and international researchers.

Customer Reviews:

Good breadth of topics, but many incorrect math.......2006-07-19

This is an important book, but it would be more useful if it didn't have so many errors in the equations. Chapter 5, for example, states that the rates are a part of the model, but the equations on pages 210-213 only define the rates, but never use them. Equation 5.2-11 should have det(Cbar) in the numerator. There are many more errors like these in just the MLANS description in Chapter 5.

mathematical models and philosophy of AI.......2001-06-22

Artificial intelligence research goes back to the 1940s as do the first developments in electronic computers. Perlovsky traces the history of AI in a thoughtful and scholarly manner, emphasizing his philosophy and his own generalization of the theory which he calls Modeling Field Theory (MFT). He also traces the study of intelligence back to the Greek philosophers beginning with Plato and Aristotle some 2300 years ago.

The book however, provides more than just a philosophy for artificial intelligence. It mixes in some very important mathematics from the disciplines of engineering, statistics and computer science. Mathematical techniques and models have been particularly useful in the solution to problems in classification, clustering, pattern recognition, rule-based expert system development, multiple-target tracking, orbit determination and Kalman filtering, and time series prediction.

Perlovsky, over the course of his career, has had a great deal of involvement in the development of this research in both his consulting work and his work at Nichols Research Corporation. I know a lot about this because, in 1980 I began working at the Aerospace Corporation in El Segundo California as a MTS (statistician). I worked on statistical problems including Kalman filtering, image processing, image recognition, orbit determination, rule-based expert systems, multiple-target tracking and target discrimination. When I moved over to manage the tracking and discrimination algorithm development for the Air Force's Space Surveillance and Tracking System I became familar with the work being done for us by contractors that included Nichols Research Corporation (NRC). I became very familar with the work of Perlovsky and his colleagues at Nichols Research who supported us from the Newport Beach, Colorado Springs and Boston offices of the company on the multiple-target tracking algorithms and the target classification algorithms. I found the work to be so interesting and of such high quality that I joined NRC in 1988.

Perlovsky sees artifical intelligence as a very practical discipline and believes that computing machines can do a good job of at least mimicking human intelligence through the use of a priori knowledge (as rules preprogrammed into the computer or a priori probability distributions) along with experience (collected data from observational or statistical designed studies) combined using algorithms (Bayes theorem, adaptive neural networks) based on the mathematical foundation of uncertainty incorporated through probability theory and/or fuzzy set theory.

In my experience, I have found rule-based expert systems to be one of the major successful developments in the field of artificial intelligence. At the heart of these systems lies the tools of mathematics and statistics, including the discriminant or classification algorithms based on multivariate Gaussian models (linear and quadratic classifiers) and the nonparametric classification algorithms (kernel discriminant algorithms and classification tree algorithms). Also, patterns can be discovered by computers through the use of clustering algorithms based on Gaussian mixture models or nonparametric techniques like nearest neighbor rules.

The Bayesian approach to statistical analysis has been useful in many areas including the Kalman filter. In Kalman filtering prior knowledge plus current data is used to update the estimate of the current state and for the prediction of the future state of a dynamic system using a simple recursive algorithm that is easily updated in the computer. Many of these developments are well characterized and developed from first principles in this text.

Perlovsky emphasizes his own work including the MLANS system which is a neural network system that incorporates important statistical ideas such as maximum likelihood, the Cramer-Rao inequality and statistical efficiency along with the neural network architecture.

Some of this work was developed by Perlovsky under an Army contract that was coincidentally managed by my brother Julian. I have always viewed this research as being successful because it applied appropriate statistical models to the real problems. I think the crucial aspects of this work are the appropriate use of the Bayesian paradigm and teh indentification of appropriate models for construction of the likelihood equations. The fundamental and well established tools of probability and statistics are the keys. In his proposals, Leonid also included ideas from fuzzy set theory and embedded his methods in an artificial neural network framework. I always thought that these modern theories (fuzzy set theory and neural networks) were gimmicks to get military funding. This may not have been a fair assessment on my part as a careful reading of this book indicates that Perlovsky honestly views these tools as important.

There are subtleties to concepts such as fuzzy set theory. Although I do not yet see its value as a substitute for measure theoretic probability theory for characterizing human uncertainty, it is possible that I just haven't thought hard enough about it. Maybe a continued reading and rereading of Perlovsky's book will help me.

This is a very interesting and unique book on artificial intelligence from a perspective that is quite different from what one find in the standard books written by computer scientists (who often do not have the deep understanding of probability and statistics that Perlovsky possesses).

Philosophy of Intelligence and Intelligence of Philosophy.......2001-01-19

The book is a fascinating review of concepts in philosophy, semiotics, and mathematics of intelligence; it is interesting for a wide audience, not just for mathematicians. The author relates mathematics and philosophy and finds detailed and specific connections, suggesting that the evolution of mathematics of intelligence during the last fifty years paralleled the evolution of philosophy of mind beginning from Plato. The author develops mathematical techniques for semiotics, which reconciles the differences among concepts in classical semiotics. He argues that *symbol* as understood in general culture is a process relating conscious and unconscious, and he proposes modifications in semiotic terminology needed to bring in correspondence varying usages of this term. A most intriguing is a chapter that connects the mathematics to Kantian theory; an ability for perceiving beauty, concludes the author, is associated with learning. A useful feature of the book is the proposed course outlines for mathematically prepared students and for non-mathematicians.

Review of Neural Networks and Intellect.......2000-12-12

While this book is not mainstream and would not be a good place to try to begin to learn AI it is thought provoking and presents a number of new ideas. Where Rod Brooks seaks to eliminate internal models Perlovsky makes them a part of his definition of intelligence. I rather liked the philosophical slant of the book. Perlovsky expects to overcome complexity using fuzzy logic. Looked at from Allen Newell's multiple levels or bands perspective fuzzy logic is an extension of classical logic and in any real computer is implemented by a lower level (underlying level) of ordinary Boolean logic. It would seem to me that different granularities at each level would accomplish the same thing. Rules in first order logic can represent subsymbolic entities just as easily as they represent macroscopic objects and actions. Perlovsky presents an interesting MLANS architecture but gives rather less detail on a hierachical version. The computational complexity of these recurrent networks looks rather high to me. Perlovsky's book is a good challenge to those who have already studied some traditional AI and connectionist literature. He criticizes nearest neighbor (and CBR) systems but it seens to me that they offer some very fast parallel implementations such as Kanerva's distributed memory and Stanfill and Waltz's memory-based reasoning.

Excellent new ideas..........2000-11-30

Perlovsky has published a large number of papers exploring limitations associated with today's main approaches to AI. This book captures the essence of those papers with the addition of developing and expanding Perlovsky's philosophy of AI.

Perlovsky takes the reader through the development of AI from its behaviouristic beginnings through Minsky's "revolution" of purely symbolic AI. He then goes through some more recent methods of combining both methods with some adaptive learning techniques. For all cases Perlovsky clearly demonstrates the inherent limitations of all methods through analytic means.

He then presents his view on a possible way forward though adaptive networks employing fuzzy logic, illustrated with some examples such as work done on SAR image analysis. Throughout the book he provides many examples and certainly this will make an excellent advanced textbook for the field of AI.

I am particularly impressed by his good overview and development of his philosophical views. After years of books by people like the Churchland's, Chalmers, Searle and Hofstadter this is finally a great example of someone who is not afraid to cut through the fluff and expose the real problem to further progress. This should be required reading for anyone looking into philosophy, in particular the philosophy of mind and science. The references are very valuable and Perlovsky and done an excellent job of listing many.

That said, there are a few points I would suggest for the next edition of this book. First, I find it odd that Perlovsky seems unaware of Chaitin's work in algorithmic information theory, particularly his book "The Limits of Mathematics". Considering Perlovsky's many references to Godel and Turing this is a glaring omission. Also, he is missing a reference to Wilson's excellent "Spikes, Decisions, and Actions : The Dynamical Foundations of Neurosciences", possibly because it is also a very recent publication. These three books belong together!

Perlovsky also needs to answer the issues posed by Susan Haack ("Deviant Logic, Fuzzy Logic : Beyond the Formalism") regarding the viability of fuzzy logic and better-define the methods that are the "correct" interpretation when applied to neural networks. I would hope to see an expansion on this theme in the future.

There are some irritating small problems, the most major being the spelling of "Plank" throughout the text; someone's spellcheck was acting up I guess...The index is a bit thin as well.

Book Description

This book constitutes, together with its companion, LNCS 2085, the refereed proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001, held in Granada, Spain, in June 2001. The 200 revised papers presented were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in sections on foundations of connectionism, biophysical models of neurons, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, artificial intelligence and congnitive processes, methodology for nets design, nets simulation and implementation, bio-inspired systems and engineering, and other applications in a variety of fields.

Dynamic Biological Networks: The Stomatogastric Nervous System (Computational Neuroscience)

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Dynamic Biological Networks: The Stomatogastric Nervous System (Computational Neuroscience)

Manufacturer: The MIT Press
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Binding: Hardcover

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ASIN: 0262082144

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This introduction to the crustacean stomatogastric nervous system (STNS) describes some of the best-understood neural networks in the animal kingdom at cellular, network, behavioral, comparative, and evolutionary levels of analysis.

The STNS is a model system for all researchers who are trying to understand how neural networks actually function. The various chapters introduce different aspects of this system, including its neuronal and synaptic properties, modulation, modeling, behavior, and evolution. The concluding chapter compares the properties of this network to other vertebrate and invertebrate networks.

Ronald M. Harris-Warrick is Professor of Neurobiology and Behavior at Cornell University. Eve Marder is Professor of Biology at Brandeis University. Allen 1. Selverston is Professor of Biology at the University of California, San Diego. Maurice Moulins is Professor of Biology at the Université de Bordeaux.

Contents: Overview, Bruce R. Johnson and Scott L. Hooper. Cellular and Synaptic Properties in the Crustacean Stomatogastric Nervous System, Daniel K. Hartline and Katherine Graubard. Neuromodulation of Stomatogastric Networks by Identified Neurons and Transmitters, Ronald M. Harris-Warrick, Frédéric Nagy, and Michael P. Nusbaum. Interactions and Combinations between Different Networks in the Stomatogastric Nervous System, Patsy S. Dickinson and Maurice Moulins. Modeling the Stomatogastric Nervous System, Eve Marder and Allen I. Selverston. Behavioral Correlates of Stomatogastric Network Function, Gina G. Turrigiano and Hans-Georg Heinzel. Comparative and Evolutionary Aspects of the Crustacean Stomatogastric System, Paul S. Katz and Kenro Tazaki. Parallels with Other Invertebrate and Vertebrate Motor Systems, Keir G. Pearson and Jan-Marino Ramirez.

Fractals of Brain, Fractals of Mind: In Searchg of a Symmetry Bond (Advances in Consciousness Research, No 7)

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Fractals of Brain, Fractals of Mind: In Searchg of a Symmetry Bond (Advances in Consciousness Research, No 7)

Manufacturer: John Benjamins Publishing Co
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ASIN: 1556191871

Graphical Models: Foundations of Neural Computation (Computational Neuroscience)

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Graphical Models: Foundations of Neural Computation (Computational Neuroscience)

Manufacturer: The MIT Press
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ASIN: 0262600420

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Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.

This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.

Contributors
H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodríguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss.

Mechanisms, Symbols, and Models Underlying Cognition: First International Work-Conference on the Interplay Between Natural and Artificial Computation, ... Part I (Lecture Notes in Computer Science)

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Mechanisms, Symbols, and Models Underlying Cognition: First International Work-Conference on the Interplay Between Natural and Artificial Computation, ... Part I (Lecture Notes in Computer Science)

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ASIN: 3540262989

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The two-volume set LNCS 3561 and LNCS 3562 constitute the refereed proceedings of the First International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2005, held in Las Palmas, Canary Islands, Spain in June 2005.

The 118 revised papers presented are thematically divided into two volumes; the first includes all the contributions mainly related with the methodological, conceptual, formal, and experimental developments in the fields of Neurophysiology and cognitive science. The second volume collects the papers related with bioinspired programming strategies and all the contributions related with the computational solutions to engineering problems in different application domains.

Models of Neural Networks I (Physics of Neural Networks)

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Models of Neural Networks I (Physics of Neural Networks)
E. Domany
Manufacturer: Springer-Verlag Telos
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ASIN: 3540594035

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This collection of articles responds to the urgent need for timely and comprehensive reviews in a multidisciplinary, rapidly developing field of research. The book starts out with an extensive introduction to the ideas used in the subsequent chapters, which are all centered around the theme of collective phenomena in neural networks: dynamics and storage capacity of networks of formal neurons with symmetric or asymmetric couplings, learning algorithms, temporal association, structured data (software), and structured nets (hardware). The style and level of this book make it particularly useful for advanced students and researchers looking for an accessible survey of today's theory of neural networks.

Parallelism, Learning, Evolution: Workshop on Evolutionary Models and Strategies, Neubiberg, Germany, March 10-11, 1989. Workshop on Parallel Processing: ... (Lecture Notes in Computer Science)

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Parallelism, Learning, Evolution: Workshop on Evolutionary Models and Strategies, Neubiberg, Germany, March 10-11, 1989. Workshop on Parallel Processing: ... (Lecture Notes in Computer Science)

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This volume presents the proceedings of a workshop on evolutionary models and strategies and another workshop on parallel processing, logic, organization, and technology, both held in Germany in 1989. In the search for new concepts relevant for parallel and distributed processing, the workshop on parallel processing included papers on aspects of space and time, representations of systems, non-Boolean logics, metrics, dynamics and structure, and superposition and uncertainties. The point was stressed that distributed representations of information may share features with quantum physics, such as the superposition principle and the uncertainty relations. Much of the volume contains material on general parallel processing machines, neural networks, and system-theoretic aspects. The material on evolutionary strategies is included because these strategies will yield important and powerful applications for parallel processing machines, and open the wayto new problem classes to be treated by computers.

Temporal-Pattern Learning in Neural Models (Lecture Notes in Biomathematics)

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Temporal-Pattern Learning in Neural Models (Lecture Notes in Biomathematics)
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Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning [An article from: Building and Environment]

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Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning [An article from: Building and Environment]
G.-H. Kim , S.-H. An , and K.-I. Kang
Manufacturer: Elsevier
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Binding: Digital

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ASIN: B000RQYXO6

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This digital document is a journal article from Building and Environment, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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Adequate estimation of construction costs is a key factor in construction projects. This paper examines the performance of three cost estimation models. The examinations are based on multiple regression analysis (MRA), neural networks (NNs), and case-based reasoning (CBR) of the data of 530 historical costs. Although the best NN estimating model gave more accurate estimating results than either the MRA or the CBR estimating models, the CBR estimating model performed better than the NN estimating model with respect to long-term use, available information from result, and time versus accuracy tradeoffs.

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