Probabilistic Reasoning in Expert Systems
Probabilistic Reasoning in Expert Systems

Author: Richard E. Neapolitan

Publisher: CreateSpace

Published: 2012-06-01

Total Pages: 448

ISBN-13: 9781477452547

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This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks, and it introduces the frequentist and Bayesian approaches to probability. Finally, it provides a critique of the maximum entropy formalism. Probabilistic Reasoning in Expert Systems was written from the perspective of a mathematician with the emphasis being on the development of theorems and algorithms. Every effort was made to make the material accessible. There are ample examples throughout the text. This text is important reading for anyone interested in both the fundamentals of Bayesian networks and in the history of how they came to be. It also provides an insightful comparison of the two most prominent approaches to probability.

Probabilistic Reasoning in Expert Systems
Probabilistic Reasoning in Expert Systems

Author: Richard E. Neapolitan

Publisher: Wiley-Interscience

Published: 1990-03-16

Total Pages: 492

ISBN-13:

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Addresses the use probability theory as a tool for designing with and implementing uncertainity reasoning. Provides many concrete algorithms, explores techniques for solving multimembership classification problems not based directly on causal networks, and offers practical recommendations, matching specific methods with sample expert systems.

Representing Uncertain Knowledge
Representing Uncertain Knowledge

Author: Paul Krause

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 287

ISBN-13: 9401120846

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The representation of uncertainty is a central issue in Artificial Intelligence (AI) and is being addressed in many different ways. Each approach has its proponents, and each has had its detractors. However, there is now an in creasing move towards the belief that an eclectic approach is required to represent and reason under the many facets of uncertainty. We believe that the time is ripe for a wide ranging, yet accessible, survey of the main for malisms. In this book, we offer a broad perspective on uncertainty and approach es to managing uncertainty. Rather than provide a daunting mass of techni cal detail, we have focused on the foundations and intuitions behind the various schools. The aim has been to present in one volume an overview of the major issues and decisions to be made in representing uncertain knowl edge. We identify the central role of managing uncertainty to AI and Expert Systems, and provide a comprehensive introduction to the different aspects of uncertainty. We then describe the rationales, advantages and limitations of the major approaches that have been taken, using illustrative examples. The book ends with a review of the lessons learned and current research di rections in the field. The intended readership will include researchers and practitioners in volved in the design and implementation of Decision Support Systems, Ex pert Systems, other Knowledge-Based Systems and in Cognitive Science.

Frontiers of Expert Systems
Frontiers of Expert Systems

Author: Chilukuri Krishna Mohan

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 303

ISBN-13: 1461545099

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The development of modern knowledge-based systems, for applications ranging from medicine to finance, necessitates going well beyond traditional rule-based programming. Frontiers of Expert Systems: Reasoning with Limited Knowledge attempts to satisfy such a need, introducing exciting and recent advances at the frontiers of the field of expert systems. Beginning with the central topics of logic, uncertainty and rule-based reasoning, each chapter in the book presents a different perspective on how we may solve problems that arise due to limitations in the knowledge of an expert system's reasoner. Successive chapters address (i) the fundamentals of knowledge-based systems, (ii) formal inference, and reasoning about models of a changing and partially known world, (iii) uncertainty and probabilistic methods, (iv) the expression of knowledge in rule-based systems, (v) evolving representations of knowledge as a system interacts with the environment, (vi) applying connectionist learning algorithms to improve on knowledge acquired from experts, (vii) reasoning with cases organized in indexed hierarchies, (viii) the process of acquiring and inductively learning knowledge, (ix) extraction of knowledge nuggets from very large data sets, and (x) interactions between multiple specialized reasoners with specialized knowledge bases. Each chapter takes the reader on a journey from elementary concepts to topics of active research, providing a concise description of several topics within and related to the field of expert systems, with pointers to practical applications and other relevant literature. Frontiers of Expert Systems: Reasoning with Limited Knowledge is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.

Approximate Reasoning in Intelligent Systems, Decision and Control
Approximate Reasoning in Intelligent Systems, Decision and Control

Author: E. Sanchez

Publisher: Elsevier

Published: 2014-05-23

Total Pages: 208

ISBN-13: 1483294382

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Documents realistic applications of approximate reasoning techniques, with emphasis placed on operational systems. The papers presented explore new areas of practical decision-making and control systems by considering important aspects of fuzzy logic theory and the latest developments in the field of expert systems. Specific fields of application covered include modelling and control, management, planning, diagnostics, finance and software. Contains 12 papers.

Introduction to Expert Systems
Introduction to Expert Systems

Author: Peter Jackson

Publisher: Addison Wesley Publishing Company

Published: 1999

Total Pages: 568

ISBN-13:

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In May 1997, IBM's Deeper Blue defeated the world chess champion Gary Kasparov, showing that an artificial intelligence system can outplay even the most skilled of human experts. Since the first expert systems appeared in the late sixties, we have seen three decades of research and development engineer human knowledge to more practical ends, in a pioneering effort that has integrated diverse areas of cognitive and computer science. Today, expert systems exist in many forms, from medical diagnosis to investment analysis and from counseling to production control. This third edition of Peter Jackson's best-selling book updates the technological base of expert systems research and embeds those developments in a wide variety of application areas. The earlier chapters have been refocused to take a more practical approach to the basic topics, while the later chapters introduce new topic areas such as case-based reasoning, connectionist systems and hybrid systems. Results in related areas, such as machine learning and reasoning with uncertainty, are also accorded a thorough treatment. The new edition contains many new examples and exercises, most of which are in CLIPS, a language that combines production rules with object-oriented programming. LISP, PROLOG and C++ are also featured where appropriate. Interesting problems are posed throughout, and are solved in exercises involving the analysis, design and implementation of CLIPS programs. This book will prove useful to a wide readership including general readers, students and teachers, software engineers and researchers. Its modular structure enables readers to follow a pathway most suited to their needs, providing them with an up-to-date account of expert systems technology. Peter Jackson is Director of Research at West Group, a division of The Thomson Corporation and the leading provider of information to the US legal market. Peter drives the application of natural language and information retrieval technologies to the information needs of law and business. Previous appointments include Principal Scientist at the McDonnell Douglas Research Laboratories in Saint Louis, Missouri, and Lecturer in the Department of Artificial Intelligence at the University of Edinburgh, Scotland.