Advanced Information Processing – serie

Chance discovery means discovering chances - the breaking points in systems, the marketing windows in business, etc. It involves determining the significance of some piece of information about an event and then using this new knowledge in decision making. The techniques developed combine data mining methods for finding rare but important events with knowledge management, groupware, and social psychology. The reader will find many applications, such as finding information on the Internet, recognizing changes in customer behavior, detecting the first signs of an imminent earthquake, etc.This first book dedicated to chance discovery covers the state of the art in the theory and methods and examines typical scenarios, and it thus appeals to researchers working on new techniques and algorithms and also to professionals dealing with real-world applications.

This book presents a framework for communication intensive autonomous agents inspired from the immune system. With the rapid increase of computational power and networking technology similarity between computer and biological systems may now practically work. The framework presented fills the need to guide the implementation of such a "biological" system. It is described in detail focusing on the field of diagnosis and control. Several sample applications and simulations are presented.

Whereas computer systems can easily handle even complicated and nonlinear mathematical models, human information processing is mainly based on linguistic knowledge. So the main advantage of using linguistic terms even with vague ranges is the intuitive interpretability of linguistic rules.Ishibuchi and his coauthors explain how classification and modeling can be handled in a human-understandable manner. They design a framework that can extract linguistic knowledge from numerical data by first identifying linguistic terms, then combining these terms into linguistic rules, and finally constructing a rule set from these linguistic rules. They combine their approach with state-of-the-art soft computing techniques such as multi-objective genetic algorithms, genetics-based machine learning, and fuzzified neural networks. Finally they demonstrate the usability of the combined techniques with various simulation results.In this largely self-contained volume, students specializing in soft computing will appreciate the detailed presentation, carefully discussed algorithms, and the many simulation experiments, while researchers will find a wealth of new design schemes, thorough analysis, and inspiring new research.

Spatial reasoning and planning is a core constituent in robotics, graphics, computer-aided design, and geographic information systems.After a review of previous work in the related areas, Liu and Daneshmend present here a unified framework for qualitative spatial representation and reasoning, which enables the generation of solutions to spatial problems where the geometric knowledge is imprecise. The approach utilizes qualitative spatial representation and reasoning integrated with a quantitative search procedure based on simulated annealing. Many graphical illustrations and detailed algorithm descriptions help the readers to comprehend the solution paths and to develop their own applications.The book is written as a self-contained text for researchers and graduate students in computer science and related engineering disciplines. The methodologies, algorithmic details, and case studies presented can be used as course material as well as a convenient reference.

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

E-Business has become a fact for almost all companies. But what are the key technologies for economically successful e-commerce? In this book readers will find all concepts that will coin tomorrows e-business: virtual sales assistants (shopbots), personalized web pages, electronic market places, vendor managed inventory, virtual organizations, supply chain management. Both technical and economic issues of these concepts are discussed in detail. Leading-edge real world applications are presented that will shape e-business mid-term. This book is a must-read for managers or technical consultants as well as researchers needing in-depth information for strategic business decisions.

Low-volume high-variety products like personalized cars or customized engines will be the key issues for manufacturing in the 21st century. The necessary control technology is based on the concept of holons, which are the units of production and behave as autonomous and cooperative agents, providing flexibility, adaptability, agility, and dynamic reconfigurability. This book presents the latest research results in agent-based manufacturing as carried out by researchers in academia and industry within the international "Holonic Manufacturing Systems" project. As this project was driven by industry, the results presented here are of vital interest not just to researchers in agent technologies or distributed artificial intelligence, but also to engineers and professionals in industry who have to respond to rapid changes and new demands in production.

Due to the ability to handle specific characteristics of economics and finance forecasting problems like e.g. non-linear relationships, behavioral changes, or knowledge-based domain segmentation, we have recently witnessed a phenomenal growth of the application of computational intelligence methodologies in this field.In this volume, Chen and Wang collected not just works on traditional computational intelligence approaches like fuzzy logic, neural networks, and genetic algorithms, but also examples for more recent technologies like e.g. rough sets, support vector machines, wavelets, or ant algorithms. After an introductory chapter with a structural description of all the methodologies, the subsequent parts describe novel applications of these to typical economics and finance problems like business forecasting, currency crisis discrimination, foreign exchange markets, or stock markets behavior.

Chance discovery means discovering chances - the breaking points in systems, the marketing windows in business, etc. It involves determining the significance of some piece of information about an event and then using this new knowledge in decision making. The techniques developed combine data mining methods for finding rare but important events with knowledge management, groupware, and social psychology. The reader will find many applications, such as finding information on the Internet, recognizing changes in customer behavior, detecting the first signs of an imminent earthquake, etc.This first book dedicated to chance discovery covers the state of the art in the theory and methods and examines typical scenarios, and it thus appeals to researchers working on new techniques and algorithms and also to professionals dealing with real-world applications.

This book presents a framework for communication intensive autonomous agents inspired from the immune system. With the rapid increase of computational power and networking technology similarity between computer and biological systems may now practically work. The framework presented fills the need to guide the implementation of such a "biological" system. It is described in detail focusing on the field of diagnosis and control. Several sample applications and simulations are presented.

Whereas computer systems can easily handle even complicated and nonlinear mathematical models, human information processing is mainly based on linguistic knowledge. So the main advantage of using linguistic terms even with vague ranges is the intuitive interpretability of linguistic rules. Ishibuchi and his coauthors explain how classification and modeling can be handled in a human-understandable manner. They design a framework that can extract linguistic knowledge from numerical data by first identifying linguistic terms, then combining these terms into linguistic rules, and finally constructing a rule set from these linguistic rules. They combine their approach with state-of-the-art soft computing techniques such as multi-objective genetic algorithms, genetics-based machine learning, and fuzzified neural networks. Finally they demonstrate the usability of the combined techniques with various simulation results.In this largely self-contained volume, students specializing in soft computing will appreciate the detailed presentation, carefully discussed algorithms, and the many simulation experiments, while researchers will find a wealth of new design schemes, thorough analysis, and inspiring new research.

Low-volume high-variety products like personalized cars or customized engines will be the key issues for manufacturing in the 21st century. The necessary control technology is based on the concept of holons, which are the units of production and behave as autonomous and cooperative agents, providing flexibility, adaptability, agility, and dynamic reconfigurability. This book presents the latest research results in agent-based manufacturing as carried out by researchers in academia and industry within the international "Holonic Manufacturing Systems" project. As this project was driven by industry, the results presented here are of vital interest not just to researchers in agent technologies or distributed artificial intelligence, but also to engineers and professionals in industry who have to respond to rapid changes and new demands in production.

Due to the ability to handle specific characteristics of economics and finance forecasting problems like e.g. non-linear relationships, behavioral changes, or knowledge-based domain segmentation, we have recently witnessed a phenomenal growth of the application of computational intelligence methodologies in this field.In this volume, Chen and Wang collected not just works on traditional computational intelligence approaches like fuzzy logic, neural networks, and genetic algorithms, but also examples for more recent technologies like e.g. rough sets, support vector machines, wavelets, or ant algorithms. After an introductory chapter with a structural description of all the methodologies, the subsequent parts describe novel applications of these to typical economics and finance problems like business forecasting, currency crisis discrimination, foreign exchange markets, or stock markets behavior.

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Spatial reasoning and planning is a core constituent in robotics, graphics, computer-aided design, and geographic information systems.After a review of previous work in the related areas, Liu and Daneshmend present here a unified framework for qualitative spatial representation and reasoning, which enables the generation of solutions to spatial problems where the geometric knowledge is imprecise. The approach utilizes qualitative spatial representation and reasoning integrated with a quantitative search procedure based on simulated annealing. Many graphical illustrations and detailed algorithm descriptions help the readers to comprehend the solution paths and to develop their own applications.The book is written as a self-contained text for researchers and graduate students in computer science and related engineering disciplines. The methodologies, algorithmic details, and case studies presented can be used as course material as well as a convenient reference.