Modelling of Heuristic Evaluation Strategies in Game Playing: Linear and Configural Effects in Othello
| dc.contributor.advisor | Norman, Kent L. | |
| dc.contributor.author | Phillips, Robert Vernon III | |
| dc.contributor.department | Psychology | |
| dc.contributor.publisher | Digital Repository at the University of Maryland | |
| dc.contributor.publisher | University of Maryland (College Park, Md) | |
| dc.date.accessioned | 2020-02-12T17:02:02Z | |
| dc.date.available | 2020-02-12T17:02:02Z | |
| dc.date.issued | 1981 | |
| dc.description.abstract | Psychological research on problem solving began with Thorndike's work on trial and error learning with cats, dogs, and monkeys. Kohler later initiated research with apes which convinced him that problems could be solved with insight. Through the 1940's, the study of human problem solving focused on general principles (following the Gestalt tradition) and S-R mechanisms to explain how people solve problems. The advent of computer technology in the 1950's spurred research in artificial intelligence, game playing, and problem solving. Formal definitions of problems outlined the components of a constituting the problem representation. This provided a framework for computer scientists to mechanize problem Solving with algorithms of search. Computer scientists met with success in developing programs to work on well-defined problems, such as games and puzzles, where the components of the problem representation are easily stated. Once the representation is adopted, solution is a matter of search. It has been shown that the efficiency of mechanized search is aided by the use of a ''heuristic evaluation function" (Nilsson, 1971), which has a form similar to psychological models applied in research on human decision making and judgment (Slovic and Lichtenstein, 1972). Samuel (1959), used a regression model of human judgment based on the knowledge of skilled checkers players in order to produce a heuristic evaluation function for a checkers playing program. Another model which can also be used to provide a heuristic evaluation function is based on Anderson's (1962) technique of functional measurement. This approach allows estimation of subjective scale values for the levels of information components relevant to playing a game. In contrast to these linear models, Edgell (1978) has argued that people can utilize configural information when making judgments, an issue which has been avoided by most decision modelling research. Samuel (1967) showed that use of configural infermation by a heuristic evaluation function can augment the skill of a checkers playing program, but the question of whether human players use such information was not researched. This paper reports one pilot experiment and two other experiments which were conducted to investigate whether people do use configural information when evaluating alternative moves in a game situation. The effects of game experience, learning, and training on use of configural information were examined. In addition, the research was conducted in a game playing situation in order to address the issue of ecological validity (Neisser, 1976) in psychological research. As Newell and Simon (1972) have argued, a good psychological theory of how a good chess player plays chess should play good chess. | en_US |
| dc.identifier | https://doi.org/10.13016/lk9w-dzb2 | |
| dc.identifier.other | ILLiad # 1364664 | |
| dc.identifier.uri | http://hdl.handle.net/1903/25562 | |
| dc.language.iso | en_US | en_US |
| dc.title | Modelling of Heuristic Evaluation Strategies in Game Playing: Linear and Configural Effects in Othello | en_US |
| dc.type | Dissertation | en_US |