Machine Learning of Heuristics

Donald Arthur Waterman · Stanford University. Computer Science Department

jan. 1968 · Stanford University

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First, a method of representing heuristics as production rules is developed which facilitates dynamic manipulation of the heuristics by the program embodying them. This representation technique permits separation of the heuristics from the program proper, provides clear identification of individual heuristics, is compatible with generalization schemes, and expedites the process of obtaining decisions from the system. Second, procedures are developed which permit a problem-solving program employing heuristics in production rule form to learn to improve its performance by evaluating and modifying existing heuristics and hypothesizing new ones, either during a special training process or during normal program operation. Third, the abovementioned representation and learning techniques are reformulated in the light of existing stimulus-response theories of learning, and five different S-R models of human heuristic learning in problem-solving environments are constructed and examined in detail. Experimental designs for testing these information processing models are also proposed and discussed. Finally, the feasibility of using the aforementioned representation and learning techniques in a complex problem-solving situation is demonstrated by applying these techniques to the problem of making the bet decision in draw poker. This application, involving the construction of a computer program, demonstrates that few production rules or training trials are needed to produce a thorough and effective set of heuristics for draw poker. (Author).