Industry
Meta-level knowledge: Overview and applications
"We define the concept of meta-level Knowledge, and illustrate it by briefly reviewing four examples that have been described in detail elsewhere. The examples include applications of the idea to tasks such as transfer of expertise from a domain expert to a program, and the maintenance and use of large Knowledge bases. We explore common themes that arise from these examples, and examine broader implications of the idea, in particular its impact on the design and construction of large programs."IJCAI 5, 920-927
The representation and use of focus in a system for understanding dialogs
Proc. IJCAI 77 VOL 1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY CAMBRIDGE, MASSACHUSETTS, USA AUGUST 22 - 25 , 1977, pp. 67-76 (reprinted in RNLP: 353-362)
An overview of OWL, a language for knowledge representation
Szolovitz, P. | Hawkinson, L. B. | Martin, W. A.
Rep. No. TM-86, Laboratory for Computer Science, Massachusetts Institute of Technology.
A Chess Combination Program Which Uses Plans
The program analyses carefully the initial situation. It creates some plans and tries to execute them. It analyses the situations deeper in the tree only if the plan fails. In that case it generates new plans correcting what is wrong in the old one. So, the program considers only natural branches of the tree. It can find combinations for which it is necessary to look more than twenty ply ahead. The paper describes the methods used for analyzing a situation and for modifying unsuccessful plans. Then we examine some results found by the program.Artificial Intelligence 8 (1977), 275-321
An experiment on inductive learning in chess end games.
In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley.
Epistemological Problems of Artificial Intelligence
"The epistemological part of Al studies what kinds of facts about the world are available to an observer with given opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from these facts. It leaves aside the heuristic problems of how to search spaces of possibilities and how to match patterns."See also: IJCAI 5, 1038-1044In Readings in Artificial Intelligence, B.L. Webber and N.J. Nilsson (eds.), Tioga Publishing, 1981.
Inference and knowledge in language comprehension.
In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley.
A theory of advice
In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley, 151-168.
Representation and understanding of text
In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley.