3. Textbook for a course in expert systems, if an emphasis is placed on Chapters 1 to 3 and on a selection of material from Chapters 4 to 7. There is also the option of using an additional commercially available sheU for a programming project. In assigning a programming project, the instructor may use any part of a great variety of books covering many subjects, such as car repair. Instructions for mostofthe "weekend mechanic" books are close stylisticaUy to expert system rules. Contents Chapter 1 gives an introduction to the subject matter; it briefly presents basic concepts, history, and some perspectives ofexpert systems. Then itpresents the architecture of an expert system and explains the stages of building an expert system. The concept of uncertainty in expert systems and the necessity of deal ing with the phenomenon are then presented. The chapter ends with the descrip tion of taxonomy ofexpert systems. Chapter 2 focuses on knowledge representation. Four basic ways to repre sent knowledge in expert systems are presented: first-order logic, production sys tems, semantic nets, and frames. Chapter 3 contains material about knowledge acquisition. Among machine learning techniques, a methodofrule learning from examples is explained in de tail. Then problems ofrule-base verification are discussed. In particular, both consistency and completeness oftherule base are presented."

3. Textbook for a course in expert systems, if an emphasis is placed on Chapters 1 to 3 and on a selection of material from Chapters 4 to 7. There is also the option of using an additional commercially available sheU for a programming project. In assigning a programming project, the instructor may use any part of a great variety of books covering many subjects, such as car repair. Instructions for mostofthe "weekend mechanic" books are close stylisticaUy to expert system rules. Contents Chapter 1 gives an introduction to the subject matter; it briefly presents basic concepts, history, and some perspectives ofexpert systems. Then itpresents the architecture of an expert system and explains the stages of building an expert system. The concept of uncertainty in expert systems and the necessity of deal ing with the phenomenon are then presented. The chapter ends with the descrip tion of taxonomy ofexpert systems. Chapter 2 focuses on knowledge representation. Four basic ways to repre sent knowledge in expert systems are presented: first-order logic, production sys tems, semantic nets, and frames. Chapter 3 contains material about knowledge acquisition. Among machine learning techniques, a methodofrule learning from examples is explained in de tail. Then problems ofrule-base verification are discussed. In particular, both consistency and completeness oftherule base are presented."

This book constitutes the refereed proceedings of the 7th International Conference on Rough Sets and Knowledge Technology, RSKT 2012, held in Chengdu, China during August 2012, as one of the co-located conferences of the 2012 Joint Rough Set Symposium, JRS 2012. The 63 revised papers (including 42 regular and 21 short papers) were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on rough sets and its generalizations, rough sets in data and knowledge processing, knowledge technology, advances in granular computing (AGC 2012 workshop), decision-theoretic rough set model and applications (special session), intelligent decision making and granular computing (special session), rough set foundations (special session).

The articles in this volume were selected for presentation at the Sixth Inter- tional Conference on Rough Sets and Current Trends in Computing (RSCTC 2008), which took place on October 23-25 in Akron, Ohio, USA. The conference is a premier event for researchersand industrial professionals interested in the theory and applications of rough sets and related methodo- gies. Since its introduction over 25 years ago by Zdzislaw Pawlak, the theory of rough sets has grown internationally and matured, leading to novel applications and theoretical works in areas such as data mining and knowledge discovery, machine learning, neural nets, granular and soft computing, Web intelligence, pattern recognition and control. The proceedings of the conferences in this - ries, as well as in Rough Sets and Knowledge Technology (RSKT), and the Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC) series report a variety of innovative applications of rough set theory and of its extensions. Since its inception, the mathematical rough set theory was closely connected to application ?elds of computer science and to other areas, such as medicine, which provided additional motivation for its further development and tested its real-life value. Consequently, rough set conferences emphasize the - teractionsandinterconnectionswith relatedresearchareas, providingforumsfor exchanging ideas and mutual learning. The latter aspect is particularly imp- tant since the development of rough set-related applications usually requires a combination of often diverse expertise in rough sets and an application ?eld

This volume contains the papers selected for presentation at the Third Inter- tional Conference on Rough Sets and Knowledge Technology (RSKT 2008) held in Chengdu, P. R. China, May 16-19, 2008. The RSKT conferences were initiated in 2006 in Chongqing, P. R. China. RSKT 2007 was held in Toronto, Canada, together with RSFDGrC 2007, as JRS 2007. The RSKT conferences aim to present state-of-the-art scienti?c - sults, encourage academic and industrial interaction, and promote collaborative research in rough sets and knowledge technology worldwide. They place emphasis on exploring synergies between rough sets and knowledge discovery, knowledge management, data mining, granular and soft computing as well as emerging application areas such as bioinformatics, cognitive informatics, and Web intel- gence, both at the level of theoretical foundations and real-life applications. RSKT 2008 focused on ?ve major research ?elds: computing theory and paradigms, knowledge technology, intelligent information processing, intelligent control, and applications. This was achieved by including in the conference program sessions on rough and soft computing, rough mereology with app- cations, dominance-based rough set approach, fuzzy-rough hybridization, gr- ular computing, logical and mathematical foundations, formal concept analysis, data mining, machine learning, intelligent information processing, bioinform- ics and cognitive informatics, Web intelligence, pattern recognition, and real-life applications of knowledge technology. A very strict quality control policy was adopted in the paper review process of RSKT 2008. Firstly, the PC Chairs - viewed all submissions.

We would like to present, with great pleasure, the ?rst volume of a new jo- nal, Transactions on Rough Sets. This journal, part of the new journal subline in the Springer-Verlag series Lecture Notes in Computer Science, is devoted to the entire spectrum of rough set related issues, starting from logical and ma- ematical foundations of rough sets, through all aspects of rough set theory and its applications, data mining, knowledge discovery and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets, theory of evidence, etc. The ?rst, pioneering papers on rough sets, written by the originator of the idea, ProfessorZdzis lawPawlak, werepublishedintheearly1980s.Weareproud to dedicate this volume to our mentor, Professor Zdzis law Pawlak, who kindly enriched this volume with his contribution on philosophical, logical, and mat- matical foundations of roughset theory. In his paper Professor Pawlakshows all over again the underlying ideas of rough set theory as well as its relations with Bayes' theorem, con?ict analysis, ?ow graphs, decision networks, and decision rules.

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 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.