Knowledge Acquisition, Knowledge Programming, and Knowledge Refinement.

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Bibliographic Details
Title: Knowledge Acquisition, Knowledge Programming, and Knowledge Refinement.
Language: English
Authors: Hayes-Roth, Frederick, Rand Corp., Santa Monica, CA.
Availability: Rand Corporation, Main St., Santa Monica, CA 90406 ($3.00).
Peer Reviewed: N
Page Count: 39
Publication Date: 1980
Sponsoring Agency: National Science Foundation, Washington, DC.
Contract Number: MCS77-03273
Report Number: Rand-R-2540-NSF
Document Type: Reports - Research
Descriptors: Artificial Intelligence, Computers, Information Theory, Man Machine Systems, Programing, Research Reports, Systems Development
Geographic Terms: U.S.; California
ISBN: 978-0-8330-0195-5
Abstract: This report describes the principal findings and recommendations of a 2-year Rand research project on machine-aided knowledge acquisition and discusses the transfer of expertise from humans to machines, as well as the functions of planning, debugging, knowledge refinement, and autonomous machine learning. The relative advantages of humans and machines in the building of intelligent systems are explained. Background and guidance is provided for policymakers concerned with the research and development of machine-based learning systems. The research method adopted emphasized iterative refinement of knowledge in response to actual experience; i.e., a machine's knowledge was acquired initially from a human who provided enough concepts, constraints, and problem-solving heuristics to define some minimal level of performance. Sixty-two references are listed. (Author/FM)
Journal Code: RIESEP1981
Entry Date: 1981
Accession Number: ED201337
Database: ERIC
Description
Abstract:This report describes the principal findings and recommendations of a 2-year Rand research project on machine-aided knowledge acquisition and discusses the transfer of expertise from humans to machines, as well as the functions of planning, debugging, knowledge refinement, and autonomous machine learning. The relative advantages of humans and machines in the building of intelligent systems are explained. Background and guidance is provided for policymakers concerned with the research and development of machine-based learning systems. The research method adopted emphasized iterative refinement of knowledge in response to actual experience; i.e., a machine's knowledge was acquired initially from a human who provided enough concepts, constraints, and problem-solving heuristics to define some minimal level of performance. Sixty-two references are listed. (Author/FM)
ISBN:978-0-8330-0195-5