Full Document

FormatFile SizeNotes
PDF file 2.2 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.


Purchase Print Copy

 FormatList Price Price
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.

Permission is given to duplicate this electronic document for personal use only, as long as it is unaltered and complete. Copies may not be duplicated for commercial purposes. Unauthorized posting of RAND PDFs to a non-RAND Web site is prohibited. RAND PDFs are protected under copyright law. For information on reprint and linking permissions, please visit the RAND Permissions page.

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.