Modeling expert problem solving in a game of chance: a Yahtzee© case study |
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Authors: | Ken Maynard,Patrick Moss,Marcus Whitehead,S. Narayanan,Matt Garay,Nathan Brannon,Raj Gopal Kantamneni,& Todd Kustra |
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Affiliation: | Department of Biomedical, Industrial, and Human Factors Engineering, Wright State University |
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Abstract: | Although developments on software agents have led to useful applications in automation of routine tasks such as electronic mail filtering, there is a scarcity of research that empirically evaluates the performance of a software agent versus that of a human reasoner, whose problem-solving capabilities the agent embodies. In the context of a game of a chance, namely Yahtzee©, we identified strategies deployed by expert human reasoners and developed a decision tree for agent development. This paper describes the computer implementation of the Yahtzee game as well as the software agent. It also presents a comparison of the performance of humans versus an automated agent. Results indicate that, in this context, the software agent embodies human expertise at a high level of fidelity. |
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Keywords: | modeling agents decision support knowledge acquisition |
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