Automatic discovery of heuristics for nondeterministic programs

Authors:
Salvatore J. Stolfo;Malcolm C. Harrison
Affiliations:
Computer Science Department, Courant Institute, New York University, New York, New York;Computer Science Department, Courant Institute, New York University, New York, New York
Venue:
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 2
Year:
1979

Citing 4
Cited 1

Control requirements for the design of production system architectures

Proceedings of the 1977 symposium on Artificial intelligence and programming languages
Learning Structural Descriptions From Examples

Learning Structural Descriptions From Examples
Use of meta level knowledge in the construction and maintenance of large knowledge bases.

Use of meta level knowledge in the construction and maintenance of large knowledge bases.
Automatic discovery of heuristics for nondeterministic programs from sample execution traces

Automatic discovery of heuristics for nondeterministic programs from sample execution traces

Search methods using heuristic strategies

IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1

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Abstract

This paper discusses one way to limit the cost of executing declaratively specified nondeterministic programs. The approach we take is to develop control heuristics for a family of problems from traces of sample solutions generated during a training session with a human expert. Algorithms have been developed which recognize a set of patterns in the sequence of 'knowledge applications' and which compile descriptions of these patterns in a control language, called CRAPS. The CRAPS descriptions generated are then used for guidance in solving subsequent problems. We describe an implementation of this approach and give the results of several experiments.