These two volumes consisting of Foundations and Applications provide the current status of theoretical and empirical developments in "computing with words". In philosophy, the twentieth century is said to be the century of language. This is mainly due to Wittgenstein who said: "The meaning of a word is its use in the language game". "The concept game is a concept with blurred edges". In the first phrase, "the language game" implies the everyday human activity with language, and in the latter, "game" simply implies an ordinary word. Thus, Wittgenstein precisely stated that a word is fuzzy in real life. Unfortunately this idea about a word was not accepted in the conventional science. We had to wait for Zadeh's fuzzy sets theory. Remembering Wittgenstein's statement, we should consider, on the one hand, the concept of "computing with words" from a philosophical point of view. It deeply relates to the everyday use of a word in which the meaning of a word is fuzzy in its nature.

With the Internet, the proliferation of Big Data, and autonomous systems, mankind has entered into an era of 'digital obesity'. In this century, computational intelligence, such as thinking machines, have been brought forth to process complex human problems in a wide scope of areas — from social sciences, economics and biology, medicine and social networks, to cyber security.The Handbook of Computational Intelligence (in two volumes) prompts readers to look at these problems from a non-traditional angle. It takes a step by step approach, supported by case studies, to explore the issues that have arisen in the process. The Handbook covers many classic paradigms, as well as recent achievements and future promising developments to solve some of these very complex problems. Volume one explores the subjects of fuzzy logic and systems, artificial neural networks, and learning systems. Volume two delves into evolutionary computation, hybrid systems, as well as the applications of computational intelligence in decision making, the process industry, robotics, and autonomous systems.This work is a 'one-stop-shop' for beginners, as well as an inspirational source for more advanced researchers. It is a useful resource for lecturers and learners alike.

The computing with Words (CW) is a well known soft computing method to find the solutions of many decision making problems in real life scenarios which consists of selective information used in natural language.

Cognitive informatics is a multidisciplinary field that acts as the bridge between natural science and information science. Specifically, it investigates the potential applications of information processing and natural intelligence to science and engineering disciplines. This collection, entitled Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence, provides emerging research topics in cognitive informatics research with a focus on such topics as reducing cognitive overload, real-time process algebra, and neural networks for iris recognition, emotion recognition in speech, and the classification of musical chords.

This book constitutes the refereed conference proceedings of the 15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2015, held in Tianjin, China in November 2015 as one of the co-located conference of the 2015 Joint Rough Set Symposium, JRS 2015. The 44 papers were carefully reviewed and selected from 97 submissions. The papers in this volume cover topics such as rough sets: the experts speak; generalized rough sets; rough sets and graphs; rough and fuzzy hybridization; granular computing; data mining and machine learning; three-way decisions; IJCRS 2015 data challenge.

In recent years rough set theory has attracted the attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Weareobservingagrowingresearchinterestinthefoundationsofroughsets, including the various logical, mathematical and philosophical aspects of rough sets. Some relationships have already been established between rough sets and other approaches, and also with a wide range of hybrid systems. As a result, rough sets are linked with decision system modeling and analysis of complex systems, fuzzy sets, neural networks, evolutionary computing, data mining and knowledge discovery, pattern recognition, machine learning, and approximate reasoning. In particular, rough sets are used in probabilistic reasoning, granular computing (including information granule calculi based on rough mereology), intelligent control, intelligent agent modeling, identi?cation of autonomous s- tems, and process speci?cation. Methods based on rough set theory alone or in combination with other - proacheshavebeendiscoveredwith awide rangeofapplicationsinsuchareasas: acoustics, bioinformatics, business and ?nance, chemistry, computer engineering (e.g., data compression, digital image processing, digital signal processing, p- allel and distributed computer systems, sensor fusion, fractal engineering), de- sion analysis and systems, economics, electrical engineering (e.g., control, signal analysis, power systems), environmental studies, informatics, medicine, mole- lar biology, musicology, neurology, robotics, social science, software engineering, spatial visualization, Web engineering, and Web mining.

This book describes an original approach to solving tasks of individual and collective choice: classification, ranking, and selection of multi-attribute objects. Object representation with multisets allows considering simultaneously numerical and symbolic variables. In group verbal decision analysis, judgments of all participants are taken into account without a compromise between contradictory. Natural language is used to describe problems and objects, formalize knowledge of experts and preferences of decision makers, and explain results. Verbal methods and technologies are more transparent, less laborious for a person, and weakly sensitive to measurement errors. The book also includes examples of applying new tools in real ill-structured high-dimensional choice tasks. It is intended for researchers, managers, consultants, analysts, and developers as well as for teachers and students of applied mathematics, computer science, information processing, engineering, economics, and management.

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. Contents: Knowledge Engineering and Soft Computing OCo An Introduction (L-Y Ding); Fuzzy Knowledge-Based Systems: Linguistic Integrity: A Framework for Fuzzy Modeling OCo AFRELI Algorithm (J Espinosa & J Vandewalle); A New Approach to Acquisition of Comprehensible Fuzzy Rules (H Ohno & T Furuhashi); Fuzzy Rule Generation with Fuzzy Singleton-Type Reasoning Method (Y Shi & M Mizumoto); Antecedent Validity Adaptation Principle for Table Look-Up Scheme (P-T Chan & A B Rad); Fuzzy Spline Interpolation in Sparse Fuzzy Rule Bases (M F Kawaguchi & M Miyakoshi); Revision Principle Applied for Approximate Reasoning (L-Y Ding et al.); Handling Null Queries with Compound Fuzzy Attributes (S-L Wang & Y-J Tsai); Fuzzy System Description Language (K Otsuka et al.); Knowledge Representation, Integration, and Discovery by Soft Computing: Knowledge Representation and Similarity Measure 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 & S Zadrozny); and other papers. Readership: Graduate students, researchers and lecturers in knowledge engineering and soft computing."

This chapter introduces the notion of qualitative belief assignment to model beliefs of human experts expressed in natural language (with linguistic labels). We show how qualitative beliefs can be efficiently combined using an extension of Dezert-Smarandache Theory (DSmT) of plausible and paradoxical quantitative reasoning to qualitative reasoning.

This book constitutes the refereed proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2003, held in Chongqing, China in May 2003. The 39 revised full papers and 75 revised short papers presented together with 2 invited keynote papers and 11 invited plenary papers were carefully reviewed and selected from a total of 245 submissions. The papers are organized in topical sections on rough sets foundations and methods; fuzzy sets and systems; granular computing; neural networks and evolutionary computing; data mining, machine learning, and pattern recognition; logics and reasoning; multi-agent systems; and Web intelligence and intelligent systems.