This book includes a selection of twelve carefully revised papers chosen from the papers accepted for presentation at the 4th IEEE/Nagoya-University World Wisepersons Workshop held in Nagoya in November 1995.
The combining of the technologies of fuzzy logic, neural networks, and evolutionary computation is expected to open up a new paradigm of machine learning for the realization of human-like information generating systems. The excellent papers presented are organized in sections on fuzzy and evolutionary computation, fuzzy and learning automata, fuzzy and neural networks, genetic algorithms, and CAM-brain.

This thoroughly refereed and well organized collection of papers is largely based on papers originally presented at the IJCAI'95 Workshop on Fuzzy Logic in AI, held in Montreal, Canada, in August 1995. Additionally, a few papers were invited in order to round off the scope and competent coverage of relevant topics.
The 20 revised full papers included are organized in sections on hybrid and novel architectures, machine learning and data mining, image processing and computer vision, and theoretical developments. Focusing on the most pressing problems of AI, the volume supports the view that fuzzy systems combined with traditional AI leads the move towards the next generation of intelligent systems.

This book is the result of some years of research carried out at the Vrije Universiteit of Amsterdam and at the Joint Research Centre of the European Commission. The awareness of actual and potential conflicts between economic progress in production, consumption, and technology and the environment has led to the concept of "sustainable development," implying that economic and ecological values are well balanced in evaluation and decision making. The linkages between ecosystems and economic systems are the focus of ecological economics. In ecological economics, a multidimensional approach to economic and policy-making is emphasised. In this book, the introduction of multicriteria decision aid techniques in the framework of ecological economics is widely discussed. Since such techniques are based on a "constructive" rationality and allow one to take into account conflictual, multidimensional, incommensurable and uncertain effects of decisions, they can be considered perfectly consistent with the methodological foundations of ecological economics. Since here the assumption is accepted that efficiency, equity and sustainability are the three conflictual values of economics, a mathematical procedure able to deal with these issues in an operational framework is developed, with a particular view on imprecise information in a practical environmental planning context. Given the problem of the differences in the measurement levels of the variables used for economic-ecological modelling, multicriteria methods able to deal with mixed information (both qualitative and quantitative measurements) can be considered particularly useful. Another problem related to the available information concerns the uncertainty (stochastic and/or fuzzy) contained in this information.

This book presents 14 rigorously reviewed revised papers selected from more than 50 submissions for the 1994 IEEE/ Nagoya-University World Wisepersons Workshop, WWW'94, held in August 1994 in Nagoya, Japan.The combination of approaches based on fuzzy logic, neural networks and genetic algorithms are expected to open a new paradigm of machine learning for the realization of human-like information processing systems. The first six papers in this volume are devoted to the combination of fuzzy logic and neural networks; four papers are on how to combine fuzzy logic and genetic algorithms. Four papers investigate challenging applications of fuzzy systems and of fuzzy-genetic algorithms.

This volume contains the thoroughly refereed and revised papers accepted for presentation at the IJCAI '91 Workshops on Fuzzy Logic and Fuzzy Control, held during the International Joint Conference on AI at Sydney, Australia in August 1991. The 14 technical contributions are devoted to several theoretical and applicational aspects of fuzzy logic and fuzzy control; they are presented in sections on theoretical aspects of fuzzy reasoning and fuzzy control, fuzzy neural networks, fuzzy control applications, fuzzy logic planning, and fuzzy circuits. In addition, there is a substantial introduction by the volume editors on the latest developments in the field that brings the papers presented into line.

The objective of this book is two-fold. Firstly, it is aimed at bringing to gether key research articles concerned with methodologies for knowledge discovery in databases and their applications. Secondly, it also contains articles discussing fundamentals of rough sets and their relationship to fuzzy sets, machine learning, management of uncertainty and systems of logic for formal reasoning about knowledge. Applications of rough sets in different areas such as medicine, logic design, image processing and expert systems are also represented. The articles included in the book are based on selected papers presented at the International Workshop on Rough Sets and Knowledge Discovery held in Banff, Canada in 1993. The primary methodological approach emphasized in the book is the mathematical theory of rough sets, a relatively new branch of mathematics concerned with the modeling and analysis of classification problems with imprecise, uncertain, or incomplete information. The methods of the theory of rough sets have applications in many sub-areas of artificial intelligence including knowledge discovery, machine learning, formal reasoning in the presence of uncertainty, knowledge acquisition, and others. This spectrum of applications is reflected in this book where articles, although centered around knowledge discovery problems, touch a number of related issues. The book is intended to provide an important reference material for students, researchers, and developers working in the areas of knowledge discovery, machine learning, reasoning with uncertainty, adaptive expert systems, and pattern classification."

This volume constitutes the proceedings of the Second Fuzzy Logic in AI Workshop, held in conjunction with IJCAI '93 in ChambA(c)ry, France in August 1993.
The book contains full revised versions of the papers presented at the workshop and covers several aspects of fuzzy logic in contributions from renowned researchers. This volume reflects the aim of the workshop, namely to serve as a platform for fruitful contacts between fuzzy engineering researchers interested in AI and AI researchers interested in fuzzy logic applications in engineering: when it comes to designing intelligent systems they are confronted with the same problems and questions and hope for the same successful results.

In the last 25 years, the fuzzy set theory has been applied in many disciplines such as operations research, management science, control theory, artificial intelligence/expert system, etc. In this volume, methods and applications of crisp, fuzzy and possibilistic multiple objective decision making are first systematically and thoroughly reviewed and classified. This state-of-the-art survey provides readers with a capsule look into the existing methods, and their characteristics and applicability to analysis of fuzzy and possibilistic programming problems. To realize practical fuzzy modelling, it presents solutions for real-world problems including production/manufacturing, location, logistics, environment management, banking/finance, personnel, marketing, accounting, agriculture economics and data analysis. This book is a guided tour through the literature in the rapidly growing fields of operations research and decision making and includes the most up-to-date bibliographical listing of literature on the topic.

In the last 25 years, the fuzzy set theory has been applied in many disciplines such as operations research, management science, control theory, artificial intelligence/expert system, etc. In this volume, methods and applications of fuzzy mathematical programming and possibilistic mathematical programming are first systematically and thoroughly reviewed and classified. This state-of-the-art survey provides readers with a capsule look into the existing methods, and their characteristics and applicability to analysis of fuzzy and possibilistic programming problems. To realize practical fuzzy modelling, we present solutions for real-world problems including production/manufacturing, transportation, assignment, game, environmental management, resource allocation, project investment, banking/finance, and agricultural economics. To improve flexibility and robustness of fuzzy mathematical programming techniques, we also present our expert decision-making support system IFLP which considers and solves all possibilities of a specific domain of (fuzzy) linear programming problems. Basic fuzzy set theories, membership functions, fuzzy decisions, operators and fuzzy arithmetic are introduced with simple numerical examples in aneasy-to-read and easy-to-follow manner. An updated bibliographical listing of 60 books, monographs or conference proceedings, and about 300 selected papers, reports or theses is presented in the end of this study.

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.

This book documents the research I conducted on the subject of Electronic Data Inter change during my time at the Institute of Business Informatics, University of Berne, Switzerland. In this effort I enjoyed a great deal of help from numerous others, in cluding professional colleagues, interview partners, and members of my family. Even though I cannot possibly mention them all, I would like to express my sincere gratitude for their selfless support. Above all, I am grateful to Prof. Dr. Gerhard Knolmayer who contributed to the book both in its formative stages and throughout its development. He has been an unwavering source of encouragement during the many difficult stages of the investigation and I greatly benefitted from our discussions of the subject matter. Moreover, he was ex tremely generous with his time in carefully reviewing all the five chapters. The fmancial support for this book came from the Hasler Foundation in Berne. I wish to thank the Foundation, and especially its Managing Director, Dr. P.A. Jaeger, for funding the empirical part of the research project. Likewise, I am grateful to the Uni versity of Berne for providing me with the necessary computer and other resources. The Institute of Business Informatics should be commended particularly for its very stim ulating work environment."

This volume contains the proceedings of the Eighth Austrian Artificial Intelligence Conference, held in Linz, Austria, in June 1993. The focus of the conference was on "Fuzzy Logic in Artificial Intelligence." The volume contains abstracts of two invited talks and full versions of 17 carefully selected papers. The invited talks were: "The role of fuzzylogic and soft computing in the conception and design of intelligent systems" by Lotfi A. Zadeh, and "A contextual approach for AI systems development" by Irina V. Ezhkova. The contributed papers are grouped into sections on theoretical issues, machine learning, expert systems, robotics and control, applications to medicine, and applications to car driving. Additionally, the volume contains descriptions of the four workshops that took place during the conference.

Yom 28. - 31. August 1990 fand die 19. Jahrestagung der Deutschen Gesellschaft fur Operations Research e. V. an der Wirtschaftsuniversitat Wien statt. Erstmals wurde diese Jahrestagung zusammen mit den anderen deutschsprachigen OR-Gesellschaften, der Gesell- schaft fur Mathematik, Okonomie und Operations Research (GMOOR), der Osterreichi- schen Gesellschaft fur Operations Research (OGOR) und der Schweizerischen Vereinigung fur Operations Research (SVOR) abgehalten. Diese internationale Tagung stand unter dem Titel Operations Research 1990. Fast 1000 Teilnehmer aus 38 Lander machten die Tagung zu einem internationalen GroBereignis. Am stiirksten vertreten waren dabei die Lander BRD (mit 381 Teilnehmern), Osterreich (91), Niederland~ (57), USA (44), Schweiz (38), Italien (35) sowie Kanada (23). Dank der geiinderten politischen Lage konnten knapp 100 Teilnehmer aus den (ehemaligen) sozialistischen Staaten die Tagung besuchen. Dies wurde ermoglicht dank der vielfiiltigen finanziellen Unterstiitzungen durch die Privatwirtschaft und durch das osterreichische Bundesministerium fur Wissenschaft und Forschung. Die Operations Research Proceedings 1990 dokumentieren nur einen kleinen Teil der iiber 600 Vortrage. Von den zur Publikation eingereichten Langfassungen konnten aufgrund der beschriinkten Seitenzahl nur 50 % angenommen werden. Zum groBen Bedauern des Pro- grammausschusses waren aufgrund der beschriinkten Seitenzahl von einer Ablehnung auch ausgezeichnete Manuskripte betroffen. Allein die Veroffentlichung der Kurzfassungen aller prasentierten Vortriige Mtte den Proceedingsband gesprengt. Aus diesem Grunde werden in diesen Proceedings erstmals keine Kurzfassungen publiziert und damit auf eine vollstandige Dokumentation der Tagung verzichtet. Es solI aber hier auf zwei weitere Publikationen hin- gewiesen werden, in denen Vortrage der Tagung Operations Research 90 enthalten sind.

This monograph is intended for an advanced undergraduate or graduate course as well as for researchers, who want a compilation of developments in this rapidly growing field of operations research. This is a sequel to our previous works: "Multiple Objective Decision Making--Methods and Applications: A state-of-the-Art Survey" (No.164 of the Lecture Notes); "Multiple Attribute Decision Making--Methods and Applications: A State-of-the-Art Survey" (No.186 of the Lecture Notes); and "Group Decision Making under Multiple Criteria--Methods and Applications" (No.281 of the Lecture Notes). In this monograph, the literature on methods of fuzzy Multiple Attribute Decision Making (MADM) has been reviewed thoroughly and critically, and classified systematically. This study provides readers with a capsule look into the existing methods, their characteristics, and applicability to the analysis of fuzzy MADM problems. The basic concepts and algorithms from the classical MADM methods have been used in the development of the fuzzy MADM methods. We give an overview of the classical MADM in Chapter II. Chapter III presents the basic concepts and mathematical operations of fuzzy set theory with simple numerical examples in a easy-to-read and easy-to-follow manner. Fuzzy MADM methods basically consist of two phases: (1) the aggregation of the performance scores with respect to all the attributes for each alternative, and (2) the rank ordering of the alternatives according to the aggregated scores.

The title of this book seems to indicate that the volume is dedicated to a very specialized and narrow area, i. e. , to the relationship between a very special type of optimization and mathematical programming. The contrary is however true. Optimization is certainly a very old and classical area which is of high concern to many disciplines. Engineering as well as management, politics as well as medicine, artificial intelligence as well as operations research, and many other fields are in one way or another concerned with optimization of designs, decisions, structures, procedures, or information processes. It is therefore not surprising that optimization has not grown in a homogeneous way in one discipline either. Traditionally, there was a distinct difference between optimization in engineering, optimization in management, and optimization as it was treated in mathematical sciences. However, for the last decades all these fields have to an increasing degree interacted and contributed to the area of optimization or decision making. In some respects, new disciplines such as artificial intelligence, descriptive decision theory, or modern operations research have facilitated, or even made possible the interaction between the different classical disciplines because they provided bridges and links between areas which had been developing and applied quite independently before. The development of optimiiation over the last decades can best be appreciated when looking at the traditional model of optimization. For a well-structured, Le.

This volume contains the 5 invited papers and 72 selected papers that were presented at the Fifth International Conference on Industrial and Engineering Applications of Artificial Intelligence. This is the first IEA/AIE conference to take place outside the USA: more than 120 papers were received from 23 countries, clearly indicating the international character of the conference series. Each paper was reviewed by at least three referees. The papers are grouped into parts on: CAM, reasoning and modelling, pattern recognition, software engineering and AI/ES, CAD, vision, verification and validation, neural networks, machine learning, fuzzy logic and control, robotics, design and architecture, configuration, finance, knowledge-based systems, knowledge representation, knowledge acquisition and language processing, reasoning and decision support, intelligent interfaces/DB and tutoring, fault diagnosis, planning and scheduling, and data/sensor fusion.

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.

Descriptive set theory has been one of the main areas of research in set theory for almost a century. This text attempts to present a largely balanced approach, which combines many elements of the different traditions of the subject. It includes a wide variety of examples, exercises (over 400), and applications, in order to illustrate the general concepts and results of the theory. This text provides a first basic course in classical descriptive set theory and covers material with which mathematicians interested in the subject for its own sake or those that wish to use it in their field should be familiar. Over the years, researchers in diverse areas of mathematics, such as logic and set theory, analysis, topology, probability theory, etc., have brought to the subject of descriptive set theory their own intuitions, concepts, terminology and notation.

This book discusses the invertibility of fuzzy topological spaces and related topics. Certain types of fuzzy topological spaces are introduced, and interrelations between them are brought forth. Various properties of invertible fuzzy topological spaces are presented, and characterizations for completely invertible fuzzy topological spaces are discussed. The relationship between homogeneity and invertibility is examined, and, subsequently, the orbits in an invertible fuzzy topological space are studied. The structure of invertible fuzzy topological spaces is investigated, and a clear picture of the inverting pairs in an invertible fuzzy topological space is introduced. Further, the related spaces such as sums, subspaces, simple extensions, quotient spaces, and product spaces of invertible fuzzy topological spaces are examined. In addition, the effect of invertibility on fuzzy topological properties like separation axioms, axioms of countability, compactness, and fuzzy connectedness in invertible fuzzy topological spaces is established. The book sketches ideas extended to the bigger canvas of L-topology in a very interesting manner.

This book presents the necessary and essential backgrounds of fuzzy set theory and linear programming, particularly a broad range of common Fuzzy Linear Programming (FLP) models and related, convenient solution techniques. These models and methods belong to three common classes of fuzzy linear programming, namely: (i) FLP problems in which all coefficients are fuzzy numbers, (ii) FLP problems in which the right-hand-side vectors and the decision variables are fuzzy numbers, and (iii) FLP problems in which the cost coefficients, the right-hand-side vectors and the decision variables are fuzzy numbers. The book essentially generalizes the well-known solution algorithms used in linear programming to the fuzzy environment. Accordingly, it can be used not only as a textbook, teaching material or reference book for undergraduate and graduate students in courses on applied mathematics, computer science, management science, industrial engineering, artificial intelligence, fuzzy information processes, and operations research, but can also serve as a reference book for researchers in these fields, especially those engaged in optimization and soft computing. For textbook purposes, it also includes simple and illustrative examples to help readers who are new to the field.

This book offers a rigorous mathematical analysis of fuzzy geometrical ideas. It demonstrates the use of fuzzy points for interpreting an imprecise location and for representing an imprecise line by a fuzzy line. Further, it shows that a fuzzy circle can be used to represent a circle when its description is not known precisely, and that fuzzy conic sections can be used to describe imprecise conic sections. Moreover, it discusses fundamental notions on fuzzy geometry, including the concepts of fuzzy line segment and fuzzy distance, as well as key fuzzy operations, and includes several diagrams and numerical illustrations to make the topic more understandable. The book fills an important gap in the literature, providing the first comprehensive reference guide on the fuzzy mathematics of imprecise image subsets and imprecise geometrical objects. Mainly intended for researchers active in fuzzy optimization, it also includes chapters relevant for those working on fuzzy image processing and pattern recognition. Furthermore, it is a valuable resource for beginners interested in basic operations on fuzzy numbers, and can be used in university courses on fuzzy geometry, dealing with imprecise locations, imprecise lines, imprecise circles, and imprecise conic sections.

In this book, a series of granular algorithms are proposed. A nature inspired granular algorithm based on Newtonian gravitational forces is proposed. A series of methods for the formation of higher-type information granules represented by Interval Type-2 Fuzzy Sets are also shown, via multiple approaches, such as Coefficient of Variation, principle of justifiable granularity, uncertainty-based information concept, and numerical evidence based. And a fuzzy granular application comparison is given as to demonstrate the differences in how uncertainty affects the performance of fuzzy information granules.

Multistage Fuzzy Control a model-based approach to fuzzy control and decision making Fuzzy techniques are used to cope with imprecision in the control process. This authoritative book explains the essential principles of fuzzy logic and describes both the theoretical and practical advantages of the new model-based, prescriptive approach. Professor Kacprzyk offers a comprehensive and in depth examination of the issues underlying multistage control and decision analysis, addressing in particular fuzzy dynamic systems, fuzzy events, fuzzy probabilities and fuzzy quantifiers. The text also comprises an introduction to the basic concepts of fuzzy sets, fuzzy logic and fuzzy systems, complemented by real-world examples of the use of the model-based prescriptive approach to improve the efficiency of fuzzy control systems. Highly experienced in fuzzy control research, the author identifies new trends in the development of fuzzy sets and their direct application to decision-making processes. Fuzzy control engineers, researchers and postgraduate students will find this an ideal reference, offering a wealth of ideas for enhancing the performance of fuzzy control systems and equipping them with the tools to resolve genuine problems. Multistage Fuzzy Control is an essential handbook for those wishing to resolve real-world problems in control and decision analysis through the use of fuzzy-logic-based methods.

It is frequently observed that most decision-making problems involve several objectives, and the aim of the decision makers is to find the best decision by fulfilling the aspiration levels of all the objectives. Multi-objective decision making is especially suitable for the design and planning steps and allows a decision maker to achieve the optimal or aspired goals by considering the various interactions of the given constraints. Multi-Objective Stochastic Programming in Fuzzy Environments discusses optimization problems with fuzzy random variables following several types of probability distributions and different types of fuzzy numbers with different defuzzification processes in probabilistic situations. The content within this publication examines such topics as waste management, agricultural systems, and fuzzy set theory. It is designed for academicians, researchers, and students.

"Fuzzy Engineering and Operations Research" is the edited outcome of the 5th International Conference on Fuzzy Information and Engineering (ICFIE2011) held during Oct. 15-17, 2011 in Chengdu, China and by the 1st academic conference in establishment of Guangdong Province Operations Research Society (GDORSC) held on Oct. 20, 2011 in Guangzhou, China. The 5th ICFIE2011, built on the success of previous conferences, and the GDORC, first held, are major Symposiums, respectively, for scientists, engineers practitioners and Operation Research (OR) researchers presenting their updated results, developments and applications in all areas of fuzzy information and engineering and OR. It aims to strengthen relations between industry research laboratories and universities, and to create a primary symposium for world scientists in Fuzziology and OR fields. The book contains 62 papers and is divided into five main parts: "Fuzzy Optimization, Logic and Information," "The mathematical Theory of Fuzzy Systems," "Fuzzy Engineering Applications and Soft Computing Methods," "OR and Fuzziology" and "Guess and Review.""