๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Building large knowledge-based systems: Representation and inference in the cyc project: D.B. Lenat and R.V. Guha

โœ Scribed by Robert Neches

Publisher
Elsevier Science
Year
1993
Tongue
English
Weight
921 KB
Volume
61
Category
Article
ISSN
0004-3702

No coin nor oath required. For personal study only.

โœฆ Synopsis

Top-level reactions

The Cyc Project is a monumental experiment in building very large knowledge-based systems. The book caused me to think a lot about how such research is done and what will be required to someday apply it. In this review, I want to focus on four issues derived from those concerns. I believe these issues are central to the enterprise of building very large knowledge-based software systems. In this first section, I will provide an overview of the book and an introduction to these four issues. Each issue will then be discussed in a section of its own.

The book is very readable sentence-by-sentence. Opinions on overall readability will depend on your tolerance level for rambling. Reading it will quickly give you a sense of the huge scope and complexity of the task that has been taken on.

๐Ÿ“œ SIMILAR VOLUMES

Building large knowledge-based systems:
Building large knowledge-based systems: Representation and inference in the cyc project: D.B. Lenat and R.V. Guha
โœ Charles Elkan; Russell Greiner ๐Ÿ“‚ Article ๐Ÿ“… 1993 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 725 KB

The book under review here, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project, describes progress so far in an attempt to build a system that is intended to exhibit general common-sense reasoning ability. This review first discusses aspects of the Cyc system, wi

Building large knowledge-based systems:
Building large knowledge-based systems: Representation and inference in the cyc project: D.B. Lenat and R.V. Guha
โœ Drew McDermott ๐Ÿ“‚ Article ๐Ÿ“… 1993 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 617 KB

The Cyc project, and this book, are based on two premises: ( 1 ) that what is holding AI back is programs' lack of explicitly represented knowledge; and (2) that the time is ripe to fix the problem by encoding large chunks of it. The project aims to verify (2) by the most forthright means possible,