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<p>Uncertainty is inevitable in problem solving and decision making. One way to reduce it is by seeking the advice of an expert. When we use computers to reduce uncertainty, the computer itself can become an expert in a specific field through a variety of methods. One such method is machine learning, which involves using a computer algorithm to capture hidden knowledge from data. We compared the prediction performances of three human track experts with those of two machine learning techniques: a decision tree building algorithm (ID3), and a neural network learning algorithm (backpropagation). For our research, we investigated a 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, a complex domain that involves about 50 performance variables for eight competing dogs in a race. For every race, each dog's past history is complete and freely available to bettors. This is a large amount of historical information-some accurate and relevant, some noisy and irrelevant-that must be filtered, selected, and analyzed to assist in making a prediction. This large search space 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.</p>
Peter Buntin Rinde, Daryl Neely, Siunie Sutjahjo, Chris Sommer, Linlin She, Hsinchun Chen, "Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing", IEEE Intelligent Systems, vol. 9, no. , pp. 21-27, December 1994, doi:10.1109/64.363260
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