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Thursday, July 23, 2020 | History

3 edition of Context knowledge representation and reasoning in the Context Interchange system found in the catalog.

Context knowledge representation and reasoning in the Context Interchange system

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Published by MIT Sloan School of Management, [Composite Information Systems Laboratory in Cambridge, Mass .
Written in English


Edition Notes

StatementStephane Bressan ... [et al.]
SeriesCISL WP -- 00-04, Sloan WP -- #4133, Working paper (Sloan School of Management) -- 4133., CISL WP -- #2000-04.
ContributionsBressan, Stephane, Sloan School of Management., Sloan School of Management. Composite Information Systems Laboratory.
The Physical Object
Pagination16 leaves :
Number of Pages16
ID Numbers
Open LibraryOL17936650M
OCLC/WorldCa45322780

KNOWLEDGE REPRESENTATION AND 1REASONING Hector e J. Levesque Department of Computer Science, University of Toronto, Toronto, Ontario M5S IA4 Canada The notion of a representation of knowledge is at heart easy to understand. It simply has to do . largely on the knowledge representation tech-nologies. As the primitive representational level at the foundation of knowledge repre-sentation languages, those technologies encounter all the issues central to knowledge representation of any variety. They are also useful exemplars because they are widely familiar to the field, and there is a.

Hallmark of knowledge-based system: the ability to be told facts about the world and adjust our behaviour correspondingly for example: read a book about canaries or rare coins Cognitive penetrability (Zenon Pylyshyn) actions that are conditioned by what is currently believed an example: we normally leave the room if we hear a fire alarm. present is a form of reasoning. Knowledge representation schemes are useless without the ability to reason with them. So, Knowledge Representation and Reasoning (KRR) Page 7. Manifesto of KRR a program has common sense if it automatically deduces for itself a System for reasoning. Prevent reasoning from \truths" to \falsities".

Knowledge Representation, Reasoning and Declarative Problem Solving [Chitta Baral] on elizrosshubbell.com *FREE* shipping on qualifying offers. Knowledge management and knowledge-based intelligence are areas of importance in today's economy and societyCited by: A Survey of Semantics-based Approaches for Context Reasoning in Ambient Intelligence Antonis Bikakis, Theodore Patkos, Grigoris Antoniou, and Dimitris Plexousakis The aim of context reasoning is to deduce new knowledge, based on the available context data. The endmost goal is to make the the context knowledge has been loaded in system.


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Context knowledge representation and reasoning in the Context Interchange system Download PDF EPUB FB2

Context Knowledge Representation and Reasoning in the Context Interchange System* STEPHANE BRESSANt, CHENG GOH, NATALIA LEVINA, STUART MADNICK, AHMED SHAH AND MICHAEL SIEGEL Massachusetts Institute of Technology, Cambridge, MAUSA Abstract. The Context Interchange Project presents a unique approach to the problem of semantic conflict reso.

Context Knowledge Representation and Reasoning in the Context Interchange System Stephane Bressan1, Cheng Goh2, Natalia Levina, Stuart Madnick, Ahmed Shah, Michael Siegel Massachusetts Institute of Technology, Cambridge, MA Abstract The Context Interchange Project presents a unique approach to the problem of semantic conflict.

Context Knowledge Representation and Reasoning in the Context Interchange System Stephane Bressan 1, Cheng Goh, Natalia Levina, Stuart Madnick, Ahmed Shah, Michael Siegel Massachusetts Institute of Technology, Cambridge, MA September 9, – MS revisions Abstract The Context Interchange Project presents a unique approach to the.

Context Knowledge Representation and Reasoning in the Context Interchange System Article (PDF Available) in Applied Intelligence 13(2) · September. The Context Interchange Project presents a unique approach to the problem of semantic conflict resolution among multiple heterogeneous data sources.

The system presents a semantically meaningful view of the data to the receivers (e.g. user applications) for all the available data elizrosshubbell.com by: Improving National and Homeland Security Through Context Knowledge Representation and Reasoning Technologies Chapter in SSRN Electronic Journal · October with 21.

This is a Wikipedia book, a collection of Wikipedia articles that can be easily saved, imported by an external electronic rendering service, and ordered as a printed book. – use symbolic knowledge representation and reasoning – But, they also use non-symbolic methods • Non-symbolic methods are covered in other courses (CS, CS, ) • This course would be better labeled as a course on Symbolic Representation and Reasoning – The non-symbolic representations are also knowledge representations.

Jan 24,  · Integrating Information from Global Systems: Knowledge Representation and Reasoning in the Context Interchange System. Date: 24 Jan - 24 Jan Venue: Auditorium, Level 3, SIMTech Tower Block, 71 Nanyang Drive. Aug 01,  · elizrosshubbell.com > Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) dedicated to.

[2] contextual reasoning and that of CMB and CxBR. Brézillon describes three major areas of knowledge representation: external knowledge, contextual knowledge, and procedural knowledge [3]. This is not different than the knowledge represented by CMB or CxBR except as a matter of semantics.

Procedural context knowledge is. 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.

Such techniques depend on the goal as well as on the context at different problem-solving stages. Thus, contextual reasoning is as important as learning for deriving models.

To delay—or, perhaps, avoid altogether—the next AI funding drought, we must find a way to. Feb 28,  · “Context Knowledge Representation and Reasoning in the Context of Applied Intelligence,” The International Journal of Artificial Intelligence, Neural Networks, and Complete Problem-Solving Technologies, Volume 12, Number 2, Sept.

pp. –Cited by: Context Reasoning Goals: •Reason about dynamic and ambiguous context information •Manage large amounts of context data in real-time •Collective intelligence and distributed reasoning. Contextual knowledge and proceduralized context In fact, it is difficult to define the concept of context without considering the people involved in a situation because, at first glance, context involves knowledge that is not explicit.

This 'explicitness' depends on the actors. Some. Knowledge Representation and Reasoning, then, is that part of AI that is concerned with how an agent uses what it knows in deciding what to do.

It is the study of thinking as a computational process. This book is an introduction to that field and the ways that it has invented create representations of knowledge, and context in which.

use of context in an area of AI called knowledge representation and reasoning (KRR), whose aim is to devise languages for representing what (intelligent) programs or agents know about their environment, and for representing the reasoning processes that allow them to.

The course work will consist of assignments a mideterm and a final exam. While portions of the assignments will be conceptual, the project-oriented section of the assignment will require implementation work using a specific knowledge representation and reasoning system.

Contextual Representation and Reasoning with Description Logics 3 as multi-context systems [7], distributed [4] or package-based description logics [2], where context structure is fixed and it is not possible to specify knowledge about con-texts, which limits their practical applicability.

We therefore propose an intermediate. context, the devices used to sense context most likely are not attached to the same computer running the application.

For example, an indoor infrared positioning system may consist of many infrared emitters and detectors in a building. The sensors must be physically distributed and cannot all be directly connected to a single machine.This paper discusses the uses of context in knowledge representation and reasoning (KRR).

We propose to partition the theories of context brought forward in KRR into two main classes, which we call divide-and-conquer and elizrosshubbell.com argue that this partition provides a possible explanation of why in KRR context is used to solve different types of problems, or to address the same Cited by: 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 Cited by: