Download eBook for Free
|PDF file||2.2 MB||
Use Adobe Acrobat Reader version 10 or higher for the best experience.
Purchase Print Copy
|Add to Cart||Paperback55 pages||$23.00||$18.40 20% Web Discount|
Intelligent systems can explore only tiny subsets of their potential external and conceptual worlds. To increase their effective capacities, they must develop efficient forms of representation, access, and operation. This Note develops several techniques that do not sacrifice expressibility, yet enable programs to semi-automatically improve themselves and thus increase their productivity. The basic source of power is the ability to predict the way that the program will be used in the future, and to tailor it to expedite such uses. Caching, abstraction, and expectation-simplified processing are principal examples of such techniques. This Note discusses the use of these and other economic principles for modern artificial intelligence systems. The analysis leads to some counterintuitive ideas (e.g., favoring redundancy over minimal storage in inheritance hierarchies).
This report is part of the RAND Corporation Note series. The note was a product of the RAND Corporation from 1979 to 1993 that reported other outputs of sponsored research for general distribution.
This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.
The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.