Machine methods for acquiring, learning, and applying knowledge

by Philip Klahr, John Burge, David J. Mostow


Purchase Print Copy

 FormatList Price Price
Add to Cart Paperback85 pages $30.00 $24.00 20% Web Discount

Recent advances in intelligent systems have emphasized the use of "expert knowledge" to solve problems. This paper describes a plan for attacking problems impeding development of such systems. The authors identify two chief problems as knowledge programming and learning. The task of knowledge programming is to create an intelligent system that does what an expert says it should. The learning problem requires criticizing and expanding current knowledge to improve system performance. In this view, learning produces new knowledge which must be accommodated to implement an improved system. This accommodation requires a capability for incremental knowledge programming. Research proposed to achieve these objectives is described. Examples are drawn from a heuristic program that plays a card game (hearts). Appendices provide details on technical issues, including: representation of knowledge and structure of knowledge bases; design of a knowledge programmer; various control methods, including caching and demons; design of a learning workbench; an illustrative learning scenario; and various learning heuristics.

This report is part of the RAND Corporation Paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.

Our mission to help improve policy and decisionmaking through research and analysis is enabled through our core values of quality and objectivity and our unwavering commitment to the highest level of integrity and ethical behavior. To help ensure our research and analysis are rigorous, objective, and nonpartisan, we subject our research publications to a robust and exacting quality-assurance process; avoid both the appearance and reality of financial and other conflicts of interest through staff training, project screening, and a policy of mandatory disclosure; and pursue transparency in our research engagements through our commitment to the open publication of our research findings and recommendations, disclosure of the source of funding of published research, and policies to ensure intellectual independence. For more information, visit

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.