Machine Learning of Heuristics
Department of Computer Science, Stanford University, 1968 - Artificial intelligence - 235 pages
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).
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ACM Communications action rules algorithm Appendix artificial intelligence attribute value axiom set BETLOW bf rules BLUFFO CALL I REPLACE CALL MY HAND CARDS WHAT CARDS catch the symbolic computational rule CSNUMBER current value dDNI decision matrix decision tree defined dONl draw poker DROP YOU WIN DUMMY error-causing rule evaluation example expected value explicit training Figure game playing game situation game tree heuristic definitions heuristic program heuristic rules hl,pl,bl human opponent hypothesized LASTBET learning system LESSP logic theory machine logical statements match minimaxing modified Newell number of action opponent's hand P0T+(2xLASTBET player poker program containing POT EQUALS prediction problem production rules proficiency test program subvector propositional calculus random redundant representing heuristics S-A connection situation description subvector variables symbolic subvector symbolic values techniques terminal node terminal symbol test node training information training rule training trials tree VDHAND vector variable WANT REPLACED WIN MY SCORE