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• I. Introduction
• 1. Nudge Nudge Wink Wink: Elements of Face-to-Face Conversation for Embodied Conversation Agents / Justine Cassell
• II. Systems
• 2. Human Conversation as a System Framework: Designing Embodied Conversational Agents / Justine Cassell, Tim Bickmore and Lee Campbell / [and others]
• 3. "May I Help You?": Designing Embodied Conversational Agent Allies / Elizabeth F. Churchill, Linda Cook and Peter Hodgson / [and others]
• 4. Task-Oriented Collaboration with Embodied Agents in Virtual Worlds / Jeff Rickel and W. Lewis Johnson
• 5. Deictic and Emotive Communication in Animated Pedagogical Agents / James C. Lester, Stuart G. Towns and Charles B. Callaway / [and others]
• 6. Performative Facial Expressions in Animated Faces / Isabella Poggi and Catherine Pelachaud
• 7. Emotion and Personality in a Conversational Agent / Gene Ball and Jack Breese
• 8. The Automated Design of Believable Dialogues for Animated Presentation Teams / Elisabeth Andre, Thomas Rist and Susanne van Mulken / [and others]
• 9. Parameterized Action Representation for Virtual Human Agents / Norman I. Badler, Rama Bindiganavale and Jan Allbeck / [and others]
• III. Evaluation
• 10. Developing and Evaluating Conversational Agents / Dominic W. Massaro, Michael M. Cohen and Jonas Beskow / [and others]
• 11. Designing and Evaluating Conversational Interfaces with Animated Characters / Sharon Oviatt and Bridget Adams
• 12. Measurement and Evaluation of Embodied Conversational Agents / Greogry A. Sanders and Jean Scholtz
• 13. Truth Is Beauty: Researching Embodied Conversational Agents / Clifford Nass, Katherine Isbister and Eun-Ju Lee.

This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Embodied conversational agents are computer-generated cartoonlike characters that demonstrate many of the same properties as humans in face-to-face conversation, including the ability to produce and respond to verbal and nonverbal communication. They constitute a type of (a) multimodal interface where the modalities are those natural to human conversation: speech, facial displays, hand gestures, and body stance; (b) software agent, insofar as they represent the computer in an interaction with a human or represent their human users in a computational environment (as avatars, for example); and (c) dialogue system where both verbal and nonverbal devices advance and regulate the dialogue between the user and the computer. With an embodied conversational agent, the visual dimension of interacting with an animated character on a screen plays an intrinsic role. Not just pretty pictures, the graphics display visual features of conversation in the same way that the face and hands do in face-to-face conversation among humans. This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Many of the chapters are written by multidisciplinary teams of psychologists, linguists, computer scientists, artists, and researchers in interface design. The authors include Elisabeth Andre, Norm Badler, Gene Ball, Justine Cassell, Elizabeth Churchill, James Lester, Dominic Massaro, Cliff Nass, Sharon Oviatt, Isabella Poggi, Jeff Rickel, and Greg Sanders.
(source: Nielsen Book Data)

• 1. Introduction
• 2. IMPACT architecture
• 3. service description language
• 4. Accessing legacy data and software
• 5. IMPACT server implementation
• 6. Agent programs
• 7. Meta agent programs
• 8. Temporal agent programs
• 9. Probabilistic agent programs
• 10. Secure agent programs
• 11. Complexity results
• 12. Implementing agents
• 13. An example application
• 14. Conclusions.

Software agents are the latest advance in the trend toward smaller, modular pieces of code, where each module performs a well-defined, focused task or set of tasks. Programmed to interact with and provide services to other agents, including humans, software agents act autonomously with prescribed backgrounds, beliefs, and operations. Systems of agents can access and manipulate heterogeneously stored data such as that found on the Internet. After a discussion of the theory of software agents, this book presents IMPACT (Interactive Maryland Platform for Agents Collaborating Together), an experimental agent infrastructure that translates formal theories of agency into a functional multiagent system that can extend legacy software code and application-specific or legacy data structures. The book describes three sample applications: a store, a self-correcting auto-pilot, and a supply chain.
(source: Nielsen Book Data)

• Preface
• 1. The learning methodology
• 2. Linear learning machines
• 3. Kernel-induced feature spaces
• 4. Generalisation theory
• 5. Optimisation theory
• 6. Support vector machines
• 7. Implementation techniques
• 8. Applications of support vector machines
• Appendix A: pseudocode for the SMO algorithm
• Appendix B: background mathematics
• Appendix C: glossary
• Appendix D: notation
• Bibliography
• Index.
• (source: Nielsen Book Data)

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
(source: Nielsen Book Data)

• Introduction
• Substrate systems
• Team member agent architecture
• Layered learning
• Learning an individual skill
• Learning a multiagent behavior
• Learning a team behavior
• Competition results
• Related work
• Conclusions and future work.

This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. The book makes four main contributions to the fields of machine learning and multiagent systems. First, it describes an architecture within which a flexible team structure allows member agents to decompose a task into flexible roles and to switch roles while acting. Second, it presents layered learning, a general-purpose machine-learning method for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable with existing machine-learning methods. Third, the book introduces a new multiagent reinforcement learning algorithm-team-partitioned, opaque-transition reinforcement learning (TPOT-RL)-designed for domains in which agents cannot necessarily observe the state-changes caused by other agents' actions. The final contribution is a fully functioning multiagent system that incorporates learning in a real-time, noisy domain with teammates and adversaries-a computer-simulated robotic soccer team. Peter Stone's work is the basis for the CMUnited Robotic Soccer Team, which has dominated recent RoboCup competitions. RoboCup not only helps roboticists to prove their theories in a realistic situation, but has drawn considerable public and professional attention to the field of intelligent robotics. The CMUnited team won the 1999 Stockholm simulator competition, outscoring its opponents by the rather impressive cumulative score of 110-0.
(source: Nielsen Book Data)

• 1. Rational Agents
• 2. The Belief-Desire-Intention Model
• 3. Introduction to LORA
• 4. LORA Defined
• 5. Properties of Rational Agents
• 6. Collective Mental States
• 7. Communication
• 8. Cooperation
• 9. Logic and Agent Theory.

One goal of modern computer science is to engineer computer programs that can act as autonomous, rational agents; software that can independently make good decisions about what actions to perform on our behalf and execute those actions. Applications range from small programs that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This book focuses on the belief-desire-intention (BDI) model of rational agents, which recognizes the primacy of beliefs, desires, and intentions in rational action. The BDI model has three distinct strengths: an underlying philosophy based on practical reasoning in humans, a software architecture that is implementable in real systems, and a family of logics that support a formal theory of rational agency.The book introduces a BDI logic called LORA (Logic of Rational Agents). In addition to the BDI component, LORA contains a temporal component, which allows one to represent the dynamics of how agents and their environments change over time, and an action component, which allows one to represent the actions that agents perform and the effects of the actions. The book shows how LORA can be used to capture many components of a theory of rational agency, including such notions as communication and cooperation.
(source: Nielsen Book Data)

• Outline of a computational theory of perceptions based on computing with words / L.A. Zadeh
• Introduction to soft computing and intelligent control systems / N.K. Sinha and M.M. Gupta
• Computational issues in intelligent control / X.D. Koutsoukos and P.J. Antsaklis
• Neural networks
• a guided tour / S. Haykin
• On generating variable structure organization using a genetic algorithm / A.K. Zaidi and A.H. Levis
• Evolutionary algorithms and neural networks / R.G.S. Asthana
• Neural networks and fuzzy systems / P. Musilek and M.M. Gupta
• Fuzzy neural networks / P. Musilek and M.M. Gupta
• A cursory look at parallel and distributed architectures and biologically inspired computing / S.K. Basu
• Developments in learning control systems / J.X. Xu [and others]
• Techniques for genetic adaptive control / W.K. Lennon and K.M. Passino
• Cooperative behavior of intelligent agents : theory and practice / L. Vlacic, A. Engwirda, and M. Kajitani
• Expert systems in process diagnosis and control / D. Popovic
• Neural networks for identification of nonlinear systems : an overview / P. Gupta and N.K. Sinha
• Sensor fusion system using recurrent fuzzy inference / F. Kobayashi [and others]
• Neurofuzzy state estimators / C.J. Harris, X. Hong, and Q. Gan.

• Soft computing paradigms for artificial vision / K.K. Shukla
• Intelligent control with neural networks / D. Popovic
• Knowledge-based adaptation of neurofuzzy models in predictive control of a heat exchanger / M. Fischer, O. Nelles, and R. Isermann
• Neural network approximation of piecewise continuous functions : application to friction compensation / R.R. Selmic and F.L. Lewis
• Fuzzy adaptive and predictive control of a thermic process / I. Skrjanc and D. Matko
• An intelligent approach to positive target identification / R.-N.P. Singh
• Adaptive agents and artificial life : insights for the power industry / S.A. Harp and T. Samad
• Truck backer-upper control using dynamic neural network / M.M Gupta and D.H. Rao
• Toward intelligent machines : future perspectives / M.M. Gupta and N.K. Sinha.

The field of soft computing is emerging from the cutting edge research over the last ten years devoted to fuzzy engineering and genetic algorithms. The subject is being called soft computing and computational intelligence. With acceptance of the research fundamentals in these important areas, the field is expanding into direct applications through engineering and systems science. This book cover the fundamentals of this emerging filed, as well as direct applications and case studies. There is a need for practicing engineers, computer scientists, and system scientists to directly apply "fuzzy" engineering into a wide array of devices and systems.
(source: Nielsen Book Data)

• Preface
• 1. The Machine That Could Learn Anything
• 2. Consider Thy Neighbor
• 3. Kepler on Mars
• 4. The Information Chicane
• 5. Fence-and-Fill Learning
• 6. Turing and the Submarines
• 7. The Relational Gulf
• 8. The Supercharged Learner
• 9. David Hume and the Crash of '87
• 10. Phases of Compression
• 11. Protorepresentational Learning
• 12. The Creativity Continuum
• References
• Index.

Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. This study of learning in autonomous agents offers a bracing intellectual adventure. Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. Although theorists of machine learning tell us that all learning methods contribute some form of bias and thus involve a degree of creativity, Thornton carries the idea much further. He describes an incremental process, recursive relational learning, in which the results of one learning step serve as the basis for the next. Very high-level recodings are then substantially the creative artifacts of the learner's own processing. Lower-level recodings are more "objective" in that their properties are more severely constrained by the source data. Thornton sees consciousness as a process at the outer fringe of relational learning, just prior to the onset of creativity. According to this view, we cannot assume consciousness to be an exclusively human phenomenon, but rather the expected feature of any cognitive mechanism able to engage in extended flights of relational learning. Thornton presents key background material in an entertaining manner, using extensive mental imagery and a minimum of mathematics. Anecdotes and dialogue add to the text's informality.
(source: Nielsen Book Data)

• 1. Speech hyphenation segmentation by means of blind source separation / Harold Szu and Charles Hsu
• 2. Higher-order moments based synthesis of supervised fourier demixing filter / Eiji Uchino, Noriaki Suetake, and Takeshi Yamakawa
• 3. Design and application of an acoustic database navigator for the intractive analysis of psychoacoustic sound archives and sound engineerint / Andreas Konig, Friedrich E. Blutner, Michael Eberhardt, and Robert Wenzel
• 4. Multilayer perceptron networks with adaptive centroid transformation / Mikko Lehtokangas
• 5. Identification and analysis for transiently evoked otoacoustic emission / Liming Li, Jinsen Lin, and Zhengguo Zhang
• 6. New reliability models based on imprecise probabilities / Lev V. Utkin and Sergey V. Gurov.

• 7. Multi-modular neural network for breast cancer detection / Huai Li and K.J. Ray Liu
• 8. Advanced neural nets for visual image communication / Harold Szu and Charles Hsu
• 9. Continuous valued techniques based on the Lagrangian method for the wire routing problem / Shakeel Ismail, Masahiro Nagamatu and Torao Yanaru
• 10. Chaotic neural networks for information processing / Charles Hsu and Harold Szu
• 11. Wavelet encoding for interactive genetic algorithm in emotional image retrieval / Joo-Young Lee and Sung-Bae Cho
• 12. Call admission control using interval arithmetic coulomb energy network / Won Don Lee, Kyunghee Lee and Hong-kee Kim.

This book presents worldwide outstanding research and recent progress in the applications of neural networks, fuzzy logic, chaos, independent component analysis, etc to fields related to speech recognition enhancement, supervised Fourier demixing noise elimination, acoustic databases, the human hearing system, cancer detection, image processing, and visual communications.
(source: Nielsen Book Data)

• 1. Evolutionary and neural synthesis of intelligent agents / by Karthik Balakrishnan and Vasant Honavar
• 2. Cellular encoding for interactive evolutionary robotics / by Frédéric Gruau and Kameel Quatramaran
• 3. The emergence of communication through synthetic evolution / by Bruce J. MacLennan
• 4. Optimization of classifiers using genetic algorithms / by J.J. Merelo, A. Prieto and F. Morán
• 5. Evolving neuro-controllers and sensors for artificial agents / by Karthik Balakrishnan and Vasant Honavar
• 6. Combined biological metaphors / by Egbert J.W. Boers and Ida G. Sprinkhuizen-Kuyper
• 7. Evolutionary neurogenesis applied to mobile robotics / by Oliver Michel
• 8. Development in neural networks / by Domenico Parisi and Stefano Nolfi
• 9. Evolution and learning in radial basis function neural networks : a hybrid approach / by Brian Carse, Terence C. Fogarty and John C.W. Sullivan
• 10. Co-evolution and ontogenetic change in competing robots / by Dario Floreano, Stefano Nolfi and Francesco Mondada
• 11. Goal directed adaptive behavior in second-order neural networks : learning and evolving in the MAXSON architecture / by Federick L. Crabbe and Michael G. Dyer
• 12. Evolving heterogeneous neural agents by local selection / by Filippo Menczer, W. Nick Street and Melania Degeratu
• 13. Learning sequential decision tasks through symbiotic evolution of neural networks / by David E. Moriarty and Risto Miikkulainen
• 14. From evolving a single neural network to evolving neural network ensembles / by Xin Yao and Yong Liu
• 15. Evolutionary synthesis of Bayesian networks for optimization / by Heinz Mühlenbein and Thilo Mahnig.

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. 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.
(source: Nielsen Book Data)

• Introduction to agent engineering, J.-M. Liu et al
• why autonomy makes the agent, S. Joseph and T. Kawamura
• knowledge granularity spectrum, action pyramid and the scaling problem, Y.-M. Ye and J.K. Tsotsos
• the motivation for dynamic decision-making frameworks in multi-agent systems, K.S. Barber and C.E. Martin
• dynamically organizing KDD processes in a multi-agent KDD system, N. Zhong et al
• self-organized intelligence, J.-M. Liu
• valuation-based coalition formation in multi-agent systems, S.J. Johansson
• simulating how to cooperate in iterated chicken and prisoner's dilemma games, B. Carlsson
• training intelligent agents using human data collected on the Internet, E. Sklar et al
• agent dynamics - soap paradigm, F.W.K. Lor.
• (source: Nielsen Book Data)

Agent engineering concerns the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating and delegating in a dynamic environment. It is one of the most promising areas of research and development in information technology, computer science and engineering.This book addresses some of the key issues in agent engineering: What is meant by "autonomous agents"? How can we build agents with autonomy? What are the desirable capabilities of agents with respect to surviving (they will not die) and living (they will furthermore enjoy their being or existence)? How can agents cooperate among themselves? In order to achieve the optimal performance at the global level, how much optimization at the local, individual level and how much at the global level would be necessary?
(source: Nielsen Book Data)

• Introduction to agent engineering, J.-M. Liu et al
• why autonomy makes the agent, S. Joseph and T. Kawamura
• knowledge granularity spectrum, action pyramid and the scaling problem, Y.-M. Ye and J.K. Tsotsos
• the motivation for dynamic decision-making frameworks in multi-agent systems, K.S. Barber and C.E. Martin
• dynamically organizing KDD processes in a multi-agent KDD system, N. Zhong et al
• self-organized intelligence, J.-M. Liu
• valuation-based coalition formation in multi-agent systems, S.J. Johansson
• simulating how to cooperate in iterated chicken and prisoner's dilemma games, B. Carlsson
• training intelligent agents using human data collected on the Internet, E. Sklar et al
• agent dynamics - soap paradigm, F.W.K. Lor.
• (source: Nielsen Book Data)

Agent engineering concerns the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating and delegating in a dynamic environment. It is one of the most promising areas of research and development in information technology, computer science and engineering.This book addresses some of the key issues in agent engineering: What is meant by "autonomous agents"? How can we build agents with autonomy? What are the desirable capabilities of agents with respect to surviving (they will not die) and living (they will furthermore enjoy their being or existence)? How can agents cooperate among themselves? In order to achieve the optimal performance at the global level, how much optimization at the local, individual level and how much at the global level would be necessary?
(source: Nielsen Book Data)

• Behavioural modelling, planning, and learning
• synthetic autonomy
• dynamics of distributed computation
• self-organized autonomy in multi-agent systems
• autonomy-oriented computation
• dynamics and complexity of autonomy-oriented computation.
• (source: Nielsen Book Data)

An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviors in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioral patterns. The behavioral patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-world tasks, some researchers have started to address the issue of multiagent cooperation. Broadly speaking, the power of autonomous agents lies in their ability to deal with unpredictable, dynamically changing environments. Agent-based systems are becoming one of the most important computer technologies, holding out many promises for solving real-world problems.The aims of this book are to provide a guided tour to the pioneering work and the major technical issues in agent research, and to give an in-depth discussion on the computational mechanisms for behavioral engineering in autonomous agents. Through a systematic examination, the book attempts to provide the general design principles for building autonomous agents and the analytical tools for modeling the emerged behavioral properties of a multiagent system.
(source: Nielsen Book Data)

• Behavioural modelling, planning, and learning
• synthetic autonomy
• dynamics of distributed computation
• self-organized autonomy in multi-agent systems
• autonomy-oriented computation
• dynamics and complexity of autonomy-oriented computation.
• (source: Nielsen Book Data)

An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviors in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioral patterns. The behavioral patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-world tasks, some researchers have started to address the issue of multiagent cooperation. Broadly speaking, the power of autonomous agents lies in their ability to deal with unpredictable, dynamically changing environments. Agent-based systems are becoming one of the most important computer technologies, holding out many promises for solving real-world problems.The aims of this book are to provide a guided tour to the pioneering work and the major technical issues in agent research, and to give an in-depth discussion on the computational mechanisms for behavioral engineering in autonomous agents. Through a systematic examination, the book attempts to provide the general design principles for building autonomous agents and the analytical tools for modeling the emerged behavioral properties of a multiagent system.
(source: Nielsen Book Data)

• Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents;
• Chapter 1. Introduction;
• Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces;
• Chapter 3. Niches in NK-Landscapes;
• Chapter 4. New Methods for Tunable, Random Landscapes;
• Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem;
• Chapter 6. Direct Statistical Estimation of GA Landscape Properties;
• Chapter 7. Comparing Population Mean Curves;
• Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment

• Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic Algorithms
• Chapter 10. Towards a Theory of Strong Overgeneral Classifiers;
• Chapter 11. Evolutionary Optimization through PAC Learning;
• Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms;
• Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach;
• Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination;
• Chapter 15. The Mixing Rate of Different Crossover Operators;
• Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms

• Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods
• Chapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index

Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems. Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones. Includes research from academia, government laboratories, and industry Contains high calibre papers which have been extensively reviewed Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field Ideal for researchers in machine learning, specifically those involved with evolutionary computation.

• Part 1: Fascinating Evolution
• Part 2: Evolutionary Computation
• Part 3: If Darwin was a Programmer
• Part 4: Evolution of Developmental Programs.
• (source: Nielsen Book Data)

An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject.
(source: Nielsen Book Data)

• Formal Agent Theoreis
• Computational Architecture and Infrastructure
• Learning and Adaptation
• Knowledge Discovery and Data Mining Agents
• Distributed Intelligence
• Agent Based Applications.
• (source: Nielsen Book Data)

This volume is an attempt to capture the essence of the state-of-the-art of intelligent agent technology and to identify the new challenges and opportunities that it is or will be facing. The most important feature of the volume is that it emphasizes a multi-faceted, holistic view of this emerging technology, from its computational foundations - in terms of models, methodologies, and tools for developing a variety of embodiments of agent-based systems - to its practical impact on tackling real-world problems.
(source: Nielsen Book Data)

• Introduction to Data Mining
• The Data Mining Process
• Data Mining Tools and Techniques
• Managing the Data Mining Project
• Modeling Data
• Deploying the Results
• The Discovery and Delivery of Knowledge for Effective Enterprise Outcomes: Knowledge Management
• Appendices: Glossary
• References
• Web Sites
• Data Mining and Knowledge Discovery Data Sets in the Public Domain
• Microsoft Solution Providers
• Summary of Knowledge Management Case Studies and Web Locations.
• (source: Nielsen Book Data)

Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes. Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management.
(source: Nielsen Book Data)

• Knowledge engineering and soft computing - an introduction, L.-Y. Ding. Part 1 Fuzzy knowledge-based systems: linguistic integrity - a framework for fuzzy modelling, AFRELI algorithm, J. Espinosa and J. Vandewalle
• a new approach to acquisition of comprehensible fuzzy rules, H. Ohno and T. Furuhashi
• fuzzy rule generation with fuzzy singleton-type reasoning method, Y. Shi and M. Mizumoto
• antecedent validity adaptation principle for table look-up scheme, P.-T. Chan and A.B. Rad
• fuzzy spline interpolation in sparse fuzzy rule bases, M.F. Kawaguchi and M. Miyakoshi
• revision principles applied for approximate reasoning, L.-Y. Ding et al
• handling null queries with compound fuzzy attributes, S.-L. Wang and Y.-J. Tsai
• fuzzy system description language, K. Otsuka et al. Part 2 Knowledge representation, integration and discovery by soft computing: knowledge representation and similarity measures in learning a vague legal concept, M.Q. Xu et al
• trend fuzzy sets and recurrent fuzzy rules for ordered dataset modelling, J.F. Baldwin et al
• approaches to the design of classification systems from numerical data and linguistic knowledge, H. Ishibuchi et al
• a clustering based on self-organizing map and knowledge discovery by neural network, K. Nakagawa et al
• probabilistic rough induction, J.-Z. Dong et al
• data mining via linguistic summaries of databases - an interactive approach, J. Kacprzyk and S. Zadrozny. (Part contents).
• (source: Nielsen Book Data)

Soft computing (SC) consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as genetic algorithms. The integration of those constituent methodologies forms the core of SC. In addition, the synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance. Together with other modern technologies, SC and its applications exert unprecedented influence on intelligent systems that mimic human intelligence in thinking, learning, reasoning, and many other aspects.Knowledge engineering (KE), which deals with knowledge acquisition, representation, validation, inferencing, explanation, and maintenance, has made significant progress recently, owing to the indefatigable efforts of researchers. Undoubtedly, the hot topics of data mining and knowledge/data discovery have injected new life into the classical AI world.This book tells readers how KE has been influenced and extended by SC and how SC will be helpful in pushing the frontier of KE further. It is intended for researchers and graduate students to use as a reference in the study of knowledge engineering and intelligent systems. The reader is expected to have a basic knowledge of fuzzy logic, neural networks, genetic algorithms, and knowledge-based systems.
(source: Nielsen Book Data)

• Knowledge engineering and soft computing - an introduction, L.-Y. Ding. Part 1 Fuzzy knowledge-based systems: linguistic integrity - a framework for fuzzy modelling, AFRELI algorithm, J. Espinosa and J. Vandewalle
• a new approach to acquisition of comprehensible fuzzy rules, H. Ohno and T. Furuhashi
• fuzzy rule generation with fuzzy singleton-type reasoning method, Y. Shi and M. Mizumoto
• antecedent validity adaptation principle for table look-up scheme, P.-T. Chan and A.B. Rad
• fuzzy spline interpolation in sparse fuzzy rule bases, M.F. Kawaguchi and M. Miyakoshi
• revision principles applied for approximate reasoning, L.-Y. Ding et al
• handling null queries with compound fuzzy attributes, S.-L. Wang and Y.-J. Tsai
• fuzzy system description language, K. Otsuka et al. Part 2 Knowledge representation, integration and discovery by soft computing: knowledge representation and similarity measures in learning a vague legal concept, M.Q. Xu et al
• trend fuzzy sets and recurrent fuzzy rules for ordered dataset modelling, J.F. Baldwin et al
• approaches to the design of classification systems from numerical data and linguistic knowledge, H. Ishibuchi et al
• a clustering based on self-organizing map and knowledge discovery by neural network, K. Nakagawa et al
• probabilistic rough induction, J.-Z. Dong et al
• data mining via linguistic summaries of databases - an interactive approach, J. Kacprzyk and S. Zadrozny. (Part contents).
• (source: Nielsen Book Data)

Soft computing (SC) consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as genetic algorithms. The integration of those constituent methodologies forms the core of SC. In addition, the synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance. Together with other modern technologies, SC and its applications exert unprecedented influence on intelligent systems that mimic human intelligence in thinking, learning, reasoning, and many other aspects.Knowledge engineering (KE), which deals with knowledge acquisition, representation, validation, inferencing, explanation, and maintenance, has made significant progress recently, owing to the indefatigable efforts of researchers. Undoubtedly, the hot topics of data mining and knowledge/data discovery have injected new life into the classical AI world.This book tells readers how KE has been influenced and extended by SC and how SC will be helpful in pushing the frontier of KE further. It is intended for researchers and graduate students to use as a reference in the study of knowledge engineering and intelligent systems. The reader is expected to have a basic knowledge of fuzzy logic, neural networks, genetic algorithms, and knowledge-based systems.
(source: Nielsen Book Data)

• Automatic detection of microcalcifications in mammograms using a fuzzy classifier, A.P. Drijarkara et al
• predictive fuzzy model for control of an artificial muscle, P.B. Petrovi
• evolutionary computation for information retrieval based on user preference, H.-G. Kim and S.-B. Cho
• fuzzy logic and neural networks approach - a way to improve overall performance of integrated heating systems, E. Entchev
• design and tuning a neurofuzzy power system stabilizer using genetic algorithms, A. Afzalian and D.A. Linkens
• an application of logic programmes with soft computing aspects to fault diagnosis in digital circuits, H. Sakai et al
• determination of the motion parameters from the perspective projection of a triangle, M.M. Sein and H. Hama. (Part contents).
• (source: Nielsen Book Data)

Soft computing has been presented not only with the theoretical developments but also with a large variety of realistic applications to consumer products and industrial systems. Application of soft computing has provided the opportunity to integrate human-like vagueness and real-life uncertainty into an otherwise hard computer program.This book highlights some of the recent developments in practical applications of soft computing in engineering problems. All the chapters have been sophisticatedly designed and revised by international experts to achieve wide but in-depth coverage.
(source: Nielsen Book Data)