Books in Artificial intelligence expert systems and knowledge based systems

41-50 of 149 results in All results

Uncertainty in Artificial Intelligence 4

• 1st Edition
• Volume 9
• June 28, 2014
• T.S. Levitt + 3 more
• English
• eBook 978-1-4832-9654-8

  9 7 8 - 1 - 4 8 3 2 - 9 6 5 4 - 8

Clearly illustrated in this volume is the current relationship between Uncertainty and AI.It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI.

Artificial Neural Networks

• 1st Edition
• June 28, 2014
• K. Mäkisara + 3 more
• English
• eBook 978-1-4832-9800-9

  9 7 8 - 1 - 4 8 3 2 - 9 8 0 0 - 9

This two-volume proceedings compiles a selection of research papers presented at the ICANN-91. The scope of the volumes is interdisciplinary, ranging from mathematics and engineering to cognitive sciences and biology. European research is well represented. Volume 1 contains all the orally presented papers, including both invited talks and submitted papers. Volume 2 contains the plenary talks and the poster presentations.

Artificial Intelligence V

• 1st Edition
• June 28, 2014
• B. du Boulay + 1 more
• English
• eBook 978-1-4832-9779-8

  9 7 8 - 1 - 4 8 3 2 - 9 7 7 9 - 8

Recent results and ongoing research in Artificial Intelligence are described in this book, with emphasis on fundamental questions in several key areas: machine learning, neural networks, automated reasoning, natural language processing, and logic methods in AI. There are also more applied papers in the fields of vision, architectures for KBS, expert systems and intelligent tutoring systems. One of the changes since AIMSA'90 has been the increased numbers of papers submitted in the fields of machine learning, neural networks and hybrid systems.One of the special features of the AIMSA series of conferences is their coverage of work across both Eastern and Western Europe and the former Soviet Union as well as papers from North America. AIMSA'92 is no exception and this volume provides a unique multi-cultural view of AI.

Principles of Artificial Intelligence

• 1st Edition
• June 28, 2014
• Nils J. Nilsson
• English
• eBook 978-1-4832-9586-2

  9 7 8 - 1 - 4 8 3 2 - 9 5 8 6 - 2

A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

Readings in Artificial Intelligence and Databases

• 1st Edition
• June 28, 2014
• John Mylopoulos + 1 more
• English
• eBook 978-0-08-088662-6

  9 7 8 - 0 - 0 8 - 0 8 8 6 6 2 - 6

The interaction of database and AI technologies is crucial to such applications as data mining, active databases, and knowledge-based expert systems. This volume collects the primary readings on the interactions, actual and potential, between these two fields. The editors have chosen articles to balance significant early research and the best and most comprehensive articles from the 1980s. An in-depth introduction discusses basic research motivations, giving a survey of the history, concepts, and terminology of the interaction. Major themes, approaches and results, open issues and future directions are all discussed, including the results of a major survey conducted by the editors of current work in industry and research labs. Thirteen sections follow, each with a short introduction.Topics examined include semantic data models with emphasis on conceptual modeling techniques for databases and information systems and the integration of data model concepts in high-level data languages, definition and maintenance of integrity constraints in databases and knowledge bases, natural language front ends, object-oriented database management systems, implementation issues such as concurrency control and error recovery, and representation of time and knowledge incompleteness from the viewpoints of databases, logic programming, and AI.

Reasoning About Plans

• 1st Edition
• June 28, 2014
• James Allen + 3 more
• English
• eBook 978-1-4832-9596-1

  9 7 8 - 1 - 4 8 3 2 - 9 5 9 6 - 1

This book presents four contributions to planning research within an integrated framework. James Allen offers a survey of his research in the field of temporal reasoning, and then describes a planning system formalized and implemented directly as an inference process in the temporal logic. Starting from the same logic, Henry Kautz develops the first formal specification of the plan recognition process and develops a powerful family of algorithms for plan recognition in complex situations. Richard Pelavin then extends the temporal logic with model operators that allow the representation to support reasoning about complex planning situations involving simultaneous interacting actions, and interaction with external events. Finally, Josh Tenenberg introduces two different formalisms of abstraction in planning systems and explores the properties of these abstraction techniques in depth.

Uncertainty in Artificial Intelligence

• 1st Edition
• June 28, 2014
• Bruce D'Ambrosio + 2 more
• English
• eBook 978-1-4832-9856-6

  9 7 8 - 1 - 4 8 3 2 - 9 8 5 6 - 6

Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference (1991) covers the papers presented at the Seventh Conference on Uncertainty in Artificial Intelligence, held on July 13-15, 1991 at the University of California at Los Angeles (UCLA). The book focuses on the processes, technologies, developments, and approaches involved in artificial intelligence. The selection first offers information on combining multiple-valued logics in modular expert systems; constraint propagation with imprecise conditional probabilities; and Bayesian networks applied to therapy monitoring. The text then examines some properties of plausible reasoning; theory refinement on Bayesian networks; combination of upper and lower probabilities; and a probabilistic analysis of marker-passing techniques for plan-recognition. The publication ponders on symbolic probabilistic inference (SPI) with continuous variables, SPI with evidence potential, and local expression languages for probabilistic dependence. Topics include local expression languages for probabilistic knowledge, evidence potential algorithm, symbolic inference with evidence potential, and SPI with continuous variables algorithm. The manuscript also takes a look at the compatibility of quantitative and qualitative representations of belief and a method for integrating utility analysis into an expert system for design evaluation under uncertainty. The selection is a valuable source of data for researchers interested in artificial intelligence.

Readings in Fuzzy Sets for Intelligent Systems

• 1st Edition
• May 12, 2014
• Didier J. Dubois + 2 more
• English
• eBook 978-1-4832-1450-4

  9 7 8 - 1 - 4 8 3 2 - 1 4 5 0 - 4

Readings in Fuzzy Sets for Intelligent Systems is a collection of readings that explore the main facets of fuzzy sets and possibility theory and their use in intelligent systems. Basic notions in fuzzy set theory are discussed, along with fuzzy control and approximate reasoning. Uncertainty and informativeness, information processing, and membership, cognition, neural networks, and learning are also considered. Comprised of eight chapters, this book begins with a historical background on fuzzy sets and possibility theory, citing some forerunners who discussed ideas or formal definitions very close to the basic notions introduced by Lotfi Zadeh (1978). The reader is then introduced to fundamental concepts in fuzzy set theory, including symmetric summation and the setting of fuzzy logic; uncertainty and informativeness; and fuzzy control. Subsequent chapters deal with approximate reasoning; information processing; decision and management sciences; and membership, cognition, neural networks, and learning. Numerical methods for fuzzy clustering are described, and adaptive inference in fuzzy knowledge networks is analyzed. This monograph will be of interest to both students and practitioners in the fields of computer science, information science, applied mathematics, and artificial intelligence.

Conformal Prediction for Reliable Machine Learning

• 1st Edition
• April 23, 2014
• Vineeth Balasubramanian + 2 more
• English
• Paperback 978-0-12-398537-8

  9 7 8 - 0 - 1 2 - 3 9 8 5 3 7 - 8
• eBook 978-0-12-401715-3

  9 7 8 - 0 - 1 2 - 4 0 1 7 1 5 - 3

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems.

Knowledge-Based Configuration

• 1st Edition
• March 31, 2014
• Alexander Felfernig + 3 more
• English
• eBook 978-0-12-415869-6

  9 7 8 - 0 - 1 2 - 4 1 5 8 6 9 - 6

Knowledge-based Configuration incorporates knowledge representation formalisms to capture complex product models and reasoning methods to provide intelligent interactive behavior with the user. This book represents the first time that corporate and academic worlds collaborate integrating research and commercial benefits of knowledge-based configuration. Foundational interdisciplinary material is provided for composing models from increasingly complex products and services. Case studies, the latest research, and graphical knowledge representations that increase understanding of knowledge-based configuration provide a toolkit to continue to push the boundaries of what configurators can do and how they enable companies and customers to thrive.