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Books in Reasoning

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11-14 of 14 results in All results

Case-Based Reasoning

  • 1st Edition
  • September 1, 1993
  • Janet Kolodner
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 4 4 9 - 0
Case-based reasoning is one of the fastest growing areas in the field of knowledge-based systems and this book, authored by a leader in the field, is the first comprehensive text on the subject. Case-based reasoning systems are systems that store information about situations in their memory. As new problems arise, similar situations are searched out to help solve these problems. Problems are understood and inferences are made by finding the closest cases in memory, comparing and contrasting the problem with those cases, making inferences based on those comparisons, and asking questions when inferences can't be made.This book presents the state of the art in case-based reasoning. The author synthesizes and analyzes a broad range of approaches, with special emphasis on applying case-based reasoning to complex real-world problem-solving tasks such as medical diagnosis, design, conflict resolution, and planning. The author's approach combines cognitive science and engineering, and is based on analysis of both expert and common-sense tasks. Guidelines for building case-based expert systems are provided, such as how to represent knowledge in cases, how to index cases for accessibility, how to implement retrieval processes for efficiency, and how to adapt old solutions to fit new situations. This book is an excellent text for courses and tutorials on case-based reasoning. It is also a useful resource for computer professionals and cognitive scientists interested in learning more about this fast-growing field.

Practical Planning

  • 1st Edition
  • September 1, 1988
  • David E. Wilkins
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 1 4 4 7 - 5
Planning, or reasoning about actions, is a fundamental element of intelligent behavior--and one that artificial intelligence has found very difficult to implement. The most well-understood approach to building planning systems has been under refinement since the late 1960s and has now reached a level of maturity where there are good prospects for building working planners.Practical Planning is an in-depth examination of this classical planning paradigm through an intensive case study of SIPE, a significantly implemented planning system. The author, the developer of SIPE, defines the planning problem in general, explains why reasoning about actions is so complex, and describes all parts of the SIPE system and the algorithms needed to achieve efficiency. Details are discussed in the context of problems and important issues in building a practical planner; discussions of how other systems address these issues are also included.Assuming only a basic background in AI, Practical Planning will be of great interest to professionals interested in incorporating planning capabilities into AI systems.

Probabilistic Reasoning in Intelligent Systems

  • 1st Edition
  • September 1, 1988
  • Judea Pearl
  • English
  • Paperback
    9 7 8 - 1 - 5 5 8 6 0 - 4 7 9 - 7
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 1 4 8 9 - 5
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Qualitative Reasoning about Physical Systems

  • 1st Edition
  • February 1, 1985
  • Daniel G Bobrow
  • English
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 9 9 2 1 - 6
This volume brings together current work on qualitative reasoning. Its publication reflects the maturity of qualitative reasoning as a research area and the growing interest in problems of reasoning about physical systems.The papers present knowledge bases for a number of very different domains, including heat flow, transistors, and digital computation. A common theme of all these papers is explaining how physical systems work. An important shared criterion is that the behavioral description must be compositional, that is the description of a system's behavior must be derivable from the structure of the system.This material should be of interest to anyone concerned with automated reasoning about the real (physical) world.