The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and includes detailed coverage of possibilistic networks - a tool that allows the user to infer results from problems with imprecise data.

One of the most important applications of graphical modelling today is data mining - the data-driven discovery and modelling of hidden patterns in large data sets. The techniques described have a wide range of industrial applications, and a quality testing programme at a major car manufacturer is included as a real-life example.

• Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data.

• Each concept is carefully explained and illustrated by examples.

• Contains all necessary background material, including modelling under uncertainty, decomposition of distributions, and graphical representation of decompositions.

• Features applications of learning graphical models from data, and problems for further research.

• Includes a comprehensive bibliography.

Graphical Models: Methods for Data Analysis and Mining will be invaluable to researchers and practitioners who use graphical models in their work. Graduate students of applied statistics, computer science and engineering will find this book provides an excellent introduction to the subject.

This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced third edition has been fully revised and expanded with new content on deep learning, scalarization methods, large-scale optimization algorithms, and collective decision-making algorithms.  Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced second edition has been fully revised and expanded with new content on swarm intelligence, deep learning, fuzzy data analysis, and discrete decision graphs. Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

This monograph is an attempt to unify existing works in the field of random sets, random variables, and linguistic random variables with respect to statistical analysis. It is intended to be a tutorial research compendium. The material of the work is mainly based on the postdoctoral thesis (Ha- bilitationsschrift) of the first author and on several papers recently published by both authors. The methods form the basis of a user-friendly software tool which supports the statistical inferenee in the presence of vague data. Parts of the manuscript have been used in courses for graduate level students of mathematics and eomputer scienees held by the first author at the Technical University of Braunschweig. The textbook is designed for readers with an advanced knowledge of mathematics. The idea of writing this book came from Professor Dr. H. Skala. Several of our students have significantly contributed to its preparation. We would like to express our gratitude to Reinhard Elsner for his support in typesetting the book, Jorg Gebhardt and Jorg Knop for preparing the drawings, Michael Eike and Jiirgen Freckmann for implementing the programming system and Giinter Lehmann and Winfried Boer for proofreading the manuscript. This work was partially supported by the Fraunhofer-Gesellschaft. We are indebted to D. Reidel Publishing Company for making the pub- lication of this book possible and would especially like to acknowledge the support whieh we received from our families on this project.

This book is for both developer and decision makers of R/3 implementation teams who need to understand in-depth and practically the benefits, financial risks and technical backgrounds of IDocs and ALE in interface development. It describes the implementation of interfaces in an R/3 roll-out, imporatnt technologies such as RFC, OLE and Workflow and common standards like EDIFACT, ANSI X.12 or XML. A large number of recipes deliver templates as a starting point for own enhancements. It is for everybody who depends on fast and cost-effective solutions for EDI and it also discusses why many EDI projects are ten times as expensive as they could be. Preparing the reader with the essential knowledge to survive the outrageously fast growing world of data communication and ecommerce via internet and intranet, the book shows in a destilled manner how enterprises using R/3 can efficiently implement Electronic Data Interchange (EDI) both with external partner and with inhouse satellite systems. This book in the tradition of IT-cookbooks, where the reader will find quick recipes and reliable information to cover all aspects of SAP Interfacing and quickly became a standard work for the R/3 world.

This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Features: provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools; contains numerous examples and definitions throughout the text; presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks; covers the latest approaches, including ant colony optimization and probabilistic graphical models; written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry.

In recent years there has been a growing interest to extend classical methods for data analysis. The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance. Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled. About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS). This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis. It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics. Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.

The primary aim of this monograph is to provide a formal framework for the representation and management of uncertainty and vagueness in the field of artificial intelligence. It puts particular emphasis on a thorough analysis of these phenomena and on the development of sound mathematical modeling approaches. Beyond this theoretical basis the scope of the book includes also implementational aspects and a valuation of existing models and systems. The fundamental ambition of this book is to show that vagueness and un certainty can be handled adequately by using measure-theoretic methods. The presentation of applicable knowledge representation formalisms and reasoning algorithms substantiates the claim that efficiency requirements do not necessar ily require renunciation of an uncompromising mathematical modeling. These results are used to evaluate systems based on probabilistic methods as well as on non-standard concepts such as certainty factors, fuzzy sets or belief functions. The book is intended to be self-contained and addresses researchers and practioneers in the field of knowledge based systems. It is in particular suit able as a textbook for graduate-level students in AI, operations research and applied probability. A solid mathematical background is necessary for reading this book. Essential parts of the material have been the subject of courses given by the first author for students of computer science and mathematics held since 1984 at the University in Braunschweig."

This book provides a critical discussion of fuzzy controllers from the perspective of classical control theory. Special emphasis is placed on topics of importance for industrial applications, including self-tuning of fuzzy controllers, optimisation and stability analysis. The text begins with a detailed introduction to fuzzy systems and control theory, and guides the reader to a thorough understanding of up-to-date research results.

The fields of similarity and preference are still broadening due to the exploration of new fields of application. This is caused by the strong impact of vagueness, imprecision, uncertainty and dominance on human and agent information, communication, planning, decision, action, and control as well as by the technical progress of the information technology itself. The topics treated in this book are of interest to computer scientists, statisticians, operations researchers, experts in AI, cognitive psychologists and economists.

The fields of similarity and preference are still broadening due to the exploration of new fields of application. This is caused by the strong impact of vagueness, imprecision, uncertainty and dominance on human and agent information, communication, planning, decision, action, and control as well as by the technical progress of the information technology itself. The topics treated in this book are of interest to computer scientists, statisticians, operations researchers, experts in AI, cognitive psychologists and economists.

This book provides a critical discussion of fuzzy controllers from the perspective of classical control theory. Special emphasis is placed on topics of importance for industrial applications, including self-tuning of fuzzy controllers, optimisation and stability analysis. The text begins with a detailed introduction to fuzzy systems and control theory, and guides the reader to a thorough understanding of up-to-date research results.

This volume contains papers read at the 7th International Workshop entitled "Intelligent Agents: Decision-Support and Planning", Udine, Italy, Sep 30th - Oct 2nd, 2004. All papers were reviewed after they were presented, and revised for final publication. As its preceding ones, this workshop took place under the auspices of the International School for the Synthesis of Expert Knowledge (ISSEK) and was held in the picturesque Palazzo del Torso of the Centre International des Sciences Mecaniques (CISM), Udine, see picture below. CISM location " Palazzo del Torso " The workshop was jointly organised by Prof. G. Delia Riccia (University of Udine), Dr. D. Dubois ( CNRS and University of Toulouse III), Prof. R. Kruse (University of Magdeburg), and Prof. H .- J. Lenz (Free University Berlin). As the workshop was an invitational one, there was no need for a call for contributed papers. Contrarily, the four organisers recruited research workers from Europe who have had an impact in the last decade on "Intelligent Agents: Decision-Support and Planning".

The German Conference on Arti?cial Intelligence is a traditional and unique yearly event which brings together the German AI community and an increasing numberofinternationalguests.WhilenotasoldasIJCAI(which?rsttookplace in 1969), KI2003 marks a tradition which o?cially began in 1975 with a wor- hop of the working group "Kunstlic .. he Intelligenz" of GI. Actually, there was one important AI conference in Germany before this, the "Fachtagung Cognitive Verfahren und Systeme" (Cognitive Methods and Systems) held in Hamburg in April 1973. Thisvolumecontainstheproceedingsofthe26thAnnualGermanConference on Arti?cial Intelligence. For the technical program we had 90 submissions from 22 countries. Out of these contributions 18 papers were accepted for oral pres- tation and 24 papers for poster presentation. The acceptance criteria were set to meet high international standards. Poster presenters were given the additional opportunity to summarize their papers in three minute spotlight presentations. Oral, spotlight as well as the poster presentations were then scheduled in an interesting conference program, summarized in the book you have before you.

This book constitutes the refereed proceedings of the 5th International Conference on Intelligent Data Analysis, IDA 2003, held in Berlin, Germany in August 2003. The 56 revised papers presented were carefully reviewed and selected from 180 submissions. The papers are organized in topical sections on machine learning, probability and topology, classification and pattern recognition, clustering, applications, modeling, and data processing.

This work is a collection of front-end research papers on data fusion and perceptions. Authors are leading European experts of Artificial Intelligence, Mathematical Statistics and/or Machine Learning. Area overlaps with Intelligent Data Analysis, which aims to unscramble latent structures in collected data: Statistical Learning, Model Selection, Information Fusion, Soccer Robots, Fuzzy Quantifiers, Emotions and Artifacts.

The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline Discovering Structures in Large Databases the book starts with a unified view on Data Mining and Statistics - A System Point of View. Two special techniques follow: Subgroup Mining, and Data Mining with Possibilistic Graphical Models. Data Fusion and Possibilistic or Fuzzy Data Analysis is the next area of interest. An overview of possibilistic logic, nonmonotonic reasoning and data fusion is given, the coherence problem between data and non-linear fuzzy models is tackled, and outlier detection based on lernaing of fuzzy models is studied. In the domain of Classification and Decomposition adaptive clustering and visualisation of high dimensional data sets is introduced. Finally, in the section Learning and Data Fusion learning of special multi-agents of virtual soccer is considered. The last topic is on data fusion based on stochastic models.

The contents of these proceedings reflect the intention of the organizers of the workshop to bring together scientists and engineers having a strong interest in interdisciplinary work in the fields of computer science, mathematics and applied statistics. Results of this collaboration are illustrated in problems dealing with neural nets, statistics and networks, classification and data mining, and (machine) learning.

This book constitutes the refereed proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning, ECSQARU-FAPR'97, held in Bad Honnef, Germany, in June 1997.
The volume presents 33 revised full papers carefully selected for inclusion in the book by the program committee as well as 12 invited contributions. Among the various aspects of human practical reasoning addressed in the papers are nonmonotonic logics, default reasoning, modal logics, belief function theory, Bayesian networks, fuzzy logic, possibility theory, inference algorithms, dynamic reasoning with partial models, and user modeling approaches.

In recent years it has become apparent that an important part of the theory of artificial intelligence is concerned with reasoning on the basis of uncertain, incomplete, or inconsistent information. A variety of formalisms have been developed, including nonmonotonic logic, fuzzy sets, possibility theory, belief functions, and dynamic models of reasoning such as belief revision and Bayesian networks. Several European research projects have been formed in the area and the first European conference was held in 1991. This volume contains the papers accepted for presentation at ECSQARU-93, the European Conference on Symbolicand Quantitative Approaches to Reasoning and Uncertainty, held at the University of Granada, Spain, November 8-10, 1993.

A variety of formalisms have been developed to address such aspects of handling imperfect knowledge as uncertainty, vagueness, imprecision, incompleteness, and partial inconsistency. Some of the most familiar approaches in this research field are nonmonotonic logics, modal logics, probability theory (Bayesian and non-Bayesian), belief function theory, and fuzzy sets and possibility theory. ESPRIT Basic Research Action 3085, entitled Defeasible Reasoning and Uncertainty Management Systems (DRUMS), aims to contribute to the elucidation of similarities and differences between these formalisms. It consists of 11 active European research groups. The European Conference on Symbolic and Quantitative Approaches to Uncertainty (ESQAU) provides a forum for these groups to meet and discuss their scientific results. This volume contains 42 contributions accepted for the ESQAU meeting held in October 1991 in Marseille, together with 12 articles presenting the activities of the DRUMS groups and two invited presentations.

This monograph is an attempt to unify existing works in the field of random sets, random variables, and linguistic random variables with respect to statistical analysis. It is intended to be a tutorial research compendium. The material of the work is mainly based on the postdoctoral thesis (Ha- bilitationsschrift) of the first author and on several papers recently published by both authors. The methods form the basis of a user-friendly software tool which supports the statistical inferenee in the presence of vague data. Parts of the manuscript have been used in courses for graduate level students of mathematics and eomputer scienees held by the first author at the Technical University of Braunschweig. The textbook is designed for readers with an advanced knowledge of mathematics. The idea of writing this book came from Professor Dr. H. Skala. Several of our students have significantly contributed to its preparation. We would like to express our gratitude to Reinhard Elsner for his support in typesetting the book, Jorg Gebhardt and Jorg Knop for preparing the drawings, Michael Eike and Jiirgen Freckmann for implementing the programming system and Giinter Lehmann and Winfried Boer for proofreading the manuscript. This work was partially supported by the Fraunhofer-Gesellschaft. We are indebted to D. Reidel Publishing Company for making the pub- lication of this book possible and would especially like to acknowledge the support whieh we received from our families on this project.

This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced third edition has been fully revised and expanded with new content on deep learning, scalarization methods, large-scale optimization algorithms, and collective decision-making algorithms. Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

The Computational Brain, das aussergewohnliche Buch uber vergleichende Forschung in den Bereichen von menschlichem Gehirn und neuesten Moglichkeiten der Computertechnologie, liegt hiermit erstmals in deutscher Sprache vor. Geschrieben von einem fuhrenden Forscherteam in den USA, ist es eine Fundgrube fur alle, die wissen wollen, was der Stand der Wissenschaft auf diesem Gebiet ist. Die Autoren fuhren die Bereiche der Neuroinformatik und Neurobiologie mit gut ausgesuchten Beispielen und der gebotenen Hintergrundinformation gekonnt zusammen. Das Buch wird somit nicht nur dem Fachwissenschaftler sondern auch dem interdisziplinaren Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht.
Ubersetzt wurde das Buch von Prof. Dr. Steffen Holldobler und Dipl.-Biol. Claudia Holldobler, einem Informatiker und einer Biologin.
Rezension in Spektrum der Wissenschaft
nr. 10, S. 122 f. im Oktober 1997
(...) Die 1992 erschienene amerikanische Originalausgabe des vorliegenden Werkes ist so erfolgreich, dass man bereits von einem Klassiker reden kann. (...)
(...) ....ist das Buch sehr zu empfehlen. In Verbindung von Neurobiologie und Neuroinformatik
konkurrenzlos, vermittelt es einiges von der Faszination theoretischer Hirnforschung, die auch in Deutschland zunehmend mehr Wissenschaftler in ihren Bann schlagt.

Rezension erschienen in: Computer Spektrum 3/1997, S. 2
(...)Das Buch wird somit nicht nur dem Fachwissenschaftler, sondern auch den interdisziplinaren Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht(...)"