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Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing

Artificial Intelligence Lab, Department of MIS, University of Arizona / For our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human experts in prediction? Our research sought to answer these questions.
Date12 1900
CreatorsChen, Hsinchun, Buntin, P., She, Linlin, Sutjahjo, S., Sommer, C., Neely, D.
Source SetsUniversity of Arizona
Detected LanguageEnglish
TypeJournal Article (Paginated)

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