Managing Uncertainty in Expert Systems

The Springer International Series in Engineering and Computer Science

Book 143
Springer Science & Business Media
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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.
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Publisher
Springer Science & Business Media
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Published on
Dec 6, 2012
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Pages
224
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ISBN
9781461539827
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Best For
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Language
English
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Genres
Computers / Intelligence (AI) & Semantics
Computers / Software Development & Engineering / General
Mathematics / History & Philosophy
Mathematics / Logic
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Content Protection
This content is DRM protected.
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Monte Carlo simulation is now a well established method for studying semiconductor devices and is particularly well suited to highlighting physical mechanisms and exploring material properties. Not surprisingly, the more completely the material properties are built into the simulation, up to and including the use of a full band structure, the more powerful is the method. Indeed, it is now becoming increasingly clear that phenomena such as reliabil ity related hot-electron effects in MOSFETs cannot be understood satisfac torily without using full band Monte Carlo. The IBM simulator DAMOCLES, therefore, represents a landmark of great significance. DAMOCLES sums up the total of Monte Carlo device modeling experience of the past, and reaches with its capabilities and opportunities into the distant future. This book, therefore, begins with a description of the IBM simulator. The second chapter gives an advanced introduction to the physical basis for Monte Carlo simulations and an outlook on why complex effects such as collisional broadening and intracollisional field effects can be important and how they can be included in the simulations. References to more basic intro the book. The third chapter ductory material can be found throughout describes a typical relationship of Monte Carlo simulations to experimental data and indicates a major difficulty, the vast number of deformation poten tials required to simulate transport throughout the entire Brillouin zone. The fourth chapter addresses possible further extensions of the Monte Carlo approach and subtleties of the electron-electron interaction.
Some twenty years have elapsed since the first attempts at planning were made by researchers in artificial intelligence. These early programs concentrated on the development of plans for the solution of puzzles or toy problems, like the rearrangement of stacks of blocks. These early programs provided the foundation for the work described in this book, the automatic generation of plans for industrial assembly. As one reads about the complex and sophisticated planners in the current gen eration, it is important to keep in mind that they are addressing real-world problems. Although these systems may become the "toy" systems of tomor row, they are providing a solid foundation for future, more general and more advanced planning tools. As demonstrated by the papers in this book, the field of computer-aided mechanical assembly planning is maturing. It now may include: • geometric descriptions of parts extracted from or compatible with CAD programs; • constraints related to part interference and the use of tools; • fixtures and jigs required for the assembly; • the nature of connectors, matings and other relations between parts; • number of turnovers required during the assembly; • handling and gripping requirements for various parts; • automatic identification of subassemblies. This is not an exhaustive list, but it serves to illustrate the complexity of some of the issues which are discussed in this book. Such issues must be considered in the design of the modern planners, as they produce desirable assembly sequences and precedence relations for assembly.
Monte Carlo simulation is now a well established method for studying semiconductor devices and is particularly well suited to highlighting physical mechanisms and exploring material properties. Not surprisingly, the more completely the material properties are built into the simulation, up to and including the use of a full band structure, the more powerful is the method. Indeed, it is now becoming increasingly clear that phenomena such as reliabil ity related hot-electron effects in MOSFETs cannot be understood satisfac torily without using full band Monte Carlo. The IBM simulator DAMOCLES, therefore, represents a landmark of great significance. DAMOCLES sums up the total of Monte Carlo device modeling experience of the past, and reaches with its capabilities and opportunities into the distant future. This book, therefore, begins with a description of the IBM simulator. The second chapter gives an advanced introduction to the physical basis for Monte Carlo simulations and an outlook on why complex effects such as collisional broadening and intracollisional field effects can be important and how they can be included in the simulations. References to more basic intro the book. The third chapter ductory material can be found throughout describes a typical relationship of Monte Carlo simulations to experimental data and indicates a major difficulty, the vast number of deformation poten tials required to simulate transport throughout the entire Brillouin zone. The fourth chapter addresses possible further extensions of the Monte Carlo approach and subtleties of the electron-electron interaction.
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.
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