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An Overview of KRL, a Knowledge Representation Language

IP.com Disclosure Number: IPCOM000128902D
Original Publication Date: 1976-Jul-01
Included in the Prior Art Database: 2005-Sep-20

Publishing Venue

Software Patent Institute

Related People

Daniel G. Bobrow: AUTHOR [+4]

Abstract

by Daniel G. Bobrow and Terry Winograd. [ Footnote ] Terry Winograd is a consultant at Xerox PARC, and is affiliated with the Stanford Artificial Intelligence Laboratory. Computer Science Department. Stanford University. This document is also being issued as Stanford Artificial Intelligence Laboratory Memo AIM-293, and will appear in the journal Cognitive Science, V. 1, No. 1, 1977. CSL-76-4 July 4, 1976 This paper describes KRL, a Knowledge Representation Language designed for use in understander systems. It outlines both the general concepts which underlie our research and the details of KRL-O, an experimental implementation of some of these concepts. KRL is an attempt to integrate procedural knowledge with a broad base of declarative forms. These forms provide a variety of ways to express the logical structure of the knowledge, in order to give flexibility in associating procedures (for memory and reasoning) with specific pieces of knowledge. and to control the relative accessibility of different facts and descriptions. The formalism for declarative knowledge is based on structured conceptual objects with associated descriptions. These objects form a network of memory units with several -different sorts of linkages, each having well-specified implications for the retrieval process. Procedures can be associated directly with the internal structure of a conceptual object. This procedural attachment allows the steps for a particular operation to be determined by characteristics of the specific entities involved. The control structure of KRL is based on the belief that the next generation of intelligent programs will integrate data-directed and goal-directed processing by using multi-processing. It provides for a priority-ordered multi-process agenda with explicit (user-provided) strategies for scheduling and resource allocation. It provides procedure directories which operate along with process frameworks to allow procedural parameterization of the fundamental system processes for building. comparing, and retrieving memory structures. Future development of KRL will include integrating procedure definition with the descriptive formalism. KEY WORDS AND PHRASES Artificial intelligence, cognitive science, knowledge representation, procedural knowledge, procedural attachment, memory structures, process frameworks

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THIS DOCUMENT IS AN APPROXIMATE REPRESENTATION OF THE ORIGINAL.

©; Xerox Palo Alto Research Center, July, 1976

An Overview of KRL, a Knowledge Representation Language

by Daniel G. Bobrow and Terry Winograd. 1 CSL-76-4 July 4, 1976

This paper describes KRL, a Knowledge Representation Language designed for use in understander systems. It outlines both the general concepts which underlie our research and the details of KRL-O, an experimental implementation of some of these concepts.

KRL is an attempt to integrate procedural knowledge with a broad base of declarative forms. These forms provide a variety of ways to express the logical structure of the knowledge, in order to give flexibility in associating procedures (for memory and reasoning) with specific pieces of knowledge. and to control the relative accessibility of different facts and descriptions. The formalism for declarative knowledge is based on structured conceptual objects with associated descriptions. These objects form a network of memory units with several -different sorts of linkages, each having well-specified implications for the retrieval process. Procedures can be associated directly with the internal structure of a conceptual object. This procedural attachment allows the steps for a particular operation to be determined by characteristics of the specific entities involved.

The control structure of KRL is based on the belief that the next generation of intelligent programs will integrate data-directed and goal-directed processing by using multi-processing. It provides for a priority-ordered multi-process agenda with explicit (user-provided) strategies for scheduling and resource allocation. It provides procedure directories which operate along with process frameworks to allow procedural parameterization of the fundamental system processes for building. comparing, and retrieving memory structures. Future development of KRL will include integrating procedure definition with the descriptive formalism.

KEY WORDS AND PHRASES

Artificial intelligence, cognitive science, knowledge representation, procedural knowledge, procedural attachment, memory structures, process frameworks

CR CATEGORIES
3.6, 3.36, 3.42

XEROX

PALO ALTO RESEARCH CENTER 3333 Couple Hill Road Palo Alto California 94304 Table of Contents

l. Why we are doing it ..... 1
2. Description as the basis for a declarative language..... 2
a) Multiple descriptions of conceptual entities..... 3
b) Descriptions based on comparison to other individuals and prototypes..... 4
c) The detailed structure of units and perspectives..... 5

1 Terry Winograd is a consultant at Xerox PARC, and is affiliated with the Stanford Artificial Intelligence Laboratory. Computer Science Department. Stanford University. This document is also being issued as Stanford Artificial Intelligence Laboratory Memo AIM-293, and will appear in the journal Cognitive Science, V. 1, No. 1, 1977.

Xerox Corporation Page 1 Jul 01, 1976

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An Overvi...