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

      Chen, Hsinchun; Buntin, P.; She, Linlin; Sutjahjo, S.; Sommer, C.; Neely, D. (IEEE, 1994-12)
      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.