Combining weak learning heuristics in general problem solvers

Authors:
T. L. McCluskey
Affiliations:
The City University, London, England
Venue:
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Year:
1987

Citing 3
Cited 2

Macro-operators: a weak method for learning

Artificial Intelligence - Lecture notes in computer science 178
Chunking in Soar: The Anatomy of a General Learning Mechanism

Machine Learning
Explanation-Based Generalization: A Unifying View

Machine Learning

Combining Macro-operators with Control Knowledge

Inductive Logic Programming
Review:

The Knowledge Engineering Review

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Abstract

This paper is concerned with state space problem solvers that achieve generality by learning strong heuristics through experience in a particular domain. We specifically consider two ways of learning by analysing past solutions that can improve future problem solving: creating macros and the chunks. A method of learning search heuristics is specified which is related to 'chunking' but which complements the use of macros within a goal directed system. An example of the creation and combined use of macros and chunks, taken from an implemented system, is described.