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Reasoning system

About: Reasoning system is a(n) research topic. Over the lifetime, 5505 publication(s) have been published within this topic receiving 152220 citation(s).


Papers
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Book

[...]

01 Jan 1988
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible 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.

15,149 citations

Journal ArticleDOI

[...]

TL;DR: An overview of the foundational issues related to case-based reasoning is given, some of the leading methodological approaches within the field are described, and the current state of the field is exemplified through pointers to some systems.
Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

5,554 citations

Book

[...]

01 Jan 1993
TL;DR: Case-based reasoning as discussed by the authors is one of the fastest growing areas in the field of knowledge-based systems and the first comprehensive text on the subject is presented by a leader in this field.
Abstract: 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.

4,671 citations

Book

[...]

14 Aug 1995
TL;DR: Reasoning About Knowledge is the first book to provide a general discussion of approaches to reasoning about knowledge and its applications to distributed systems, artificial intelligence, and game theory.
Abstract: A model for knowledge and its properties completeness and complexity - results and techniques knowledge in distributed systems actions and protocols common knowledge, co-ordination and agreement evolving knowledge dealing with logical omniscience knowledge and computation common knowledge revisited.

4,196 citations

Book

[...]

01 Apr 1986
TL;DR: The third edition of Peter Jackson's book, Introduction to Expert Systems, updates the technological base of expert systems research and embeds those results in the context of a wide variety of application areas.
Abstract: From the Publisher: The third edition of Peter Jackson's book, Introduction to Expert Systems, updates the technological base of expert systems research and embeds those results in the context of a wide variety of application areas. The earlier chapters take a more practical approach to the basic topics than the previous editions, 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.

1,510 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202117
202040
201937
201839
2017122
2016151