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Author

Levesque

Bio: Levesque is an academic researcher. The author has contributed to research in topics: Frame (artificial intelligence) & Knowledge representation and reasoning. The author has an hindex of 1, co-authored 1 publications receiving 380 citations.

Papers
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Journal ArticleDOI
TL;DR: The authors have developed a design strategy for avoiding these types of problems and have implemented a representation system based on it, called Krypton, which clearly distinguishes between definitional and factual information.
Abstract: A great deal of effort has focused on developing frame-based languages for knowledge representation. While the basic ideas of frame systems are straightforward, complications arise in their design and use. The authors have developed a design strategy for avoiding these types of problems and have implemented a representation system based on it. The system, called Krypton, clearly distinguishes between definitional and factual information. In particular, Krypton has two representation languages, one for forming descriptive terms and one for making statements about the world using these terms. Further, Krypton provides a functional view of a knowledge base, characterized in terms of what it can be asked or told, rather than in terms of the particular structures it uses to represent knowledge. 11 references.

383 citations


Cited by
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Book
18 Nov 2009
TL;DR: This introduction presents the main motivations for the development of Description Logics as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition.
Abstract: This introduction presents the main motivations for the development of Description Logics (DLs) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it. We first address the relationship between Description Logics and earlier semantic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subsequently, we introduce the basic features of DL languages and related reasoning techniques. DL languages are then viewed as the core of knowledge representation systems, considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed. Finally, we address the relationship of Description Logics to other fields of Computer Science.We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.

1,966 citations

Journal ArticleDOI
TL;DR: KL-ONE as mentioned in this paper is a system for representing knowledge in Artificial Intelligence programs and has been used in both basic research and implemented knowledge-based systems in a number of places in the AI community.

1,719 citations

Book
01 Jan 2004
TL;DR: This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way, and offers the first true synthesis of the field in over a decade.
Abstract: Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs. *Authors are well-recognized experts in the field who have applied the techniques to real-world problems * Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems *Offers the first true synthesis of the field in over a decade Table of Contents 1 Introduction * 2 The Language of First-Order Logic *3 Expressing Knowledge * 4 Resolution * 5 Horn Logic * 6 Procedural Control of Reasoning * 7 Rules in Production Systems * 8 Object-Oriented Representation * 9 Structured Descriptions * 10 Inheritance * 11 Numerical Uncertainty *12 Defaults *13 Abductive Reasoning *14 Actions * 15 Planning *16 A Knowledge Representation Tradeoff * Bibliography * Index

938 citations

Journal ArticleDOI
TL;DR: A frame-based representation facility contributes to a knowledge system's ability to reason and can assist the system designer in determining strategies for controlling the system's reasoning.
Abstract: A frame-based representation facility contributes to a knowledge system's ability to reason and can assist the system designer in determining strategies for controlling the system's reasoning.

858 citations