"An important research problem in artificial intelligence is the study of methods for learning general concepts or rules from a set of training instances. An approach to this problem is presented which is guaranteed to find, without backtracing, all rule versions consistent with a set of positive and negative training instances. The algorithm put forth uses a representation of the space of those rules consistent with the observed training data. This "rule version space" is modified in response to new training instances by eliminating candidate rule versions found to conflict with each new instance. The use of version spaces is discussed in the context of Meta-DENDRAL, a program which learns rules in the domain of chemical spectroscopy."Proc. IJCAI 77 VOL 1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY CAMBRIDGE, MASSACHUSETTS, USA AUGUST 22 - 25 , 1977, pp.305-310

See also: Stanford Heuristic Programming Project Memo HPP-77-25Proc. IJCAI-77: Fifth International Joint Conference on Artificial Intelligence, pp 1014-1029

Proc. IJCAI 77 VOL 1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY CAMBRIDGE, MASSACHUSETTS, USA AUGUST 22 - 25 , 1977, pp.428-433

Proc. IJCAI 77 VOL 1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY CAMBRIDGE, MASSACHUSETTS, USA AUGUST 22 - 25 , 1977, pp. 151-157.

In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley.

"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

In Frey, 1-33.

Proc. IJCAI 77 VOL 1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY CAMBRIDGE, MASSACHUSETTS, USA AUGUST 22 - 25 , 1977, pp. 67-76 (reprinted in RNLP: 353-362)

Rep. No. TM-86, Laboratory for Computer Science, Massachusetts Institute of Technology.

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