Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing
| dc.contributor.author | Chen, Hsinchun | |
| dc.contributor.author | Buntin, P. | |
| dc.contributor.author | She, Linlin | |
| dc.contributor.author | Sutjahjo, S. | |
| dc.contributor.author | Sommer, C. | |
| dc.contributor.author | Neely, D. | |
| dc.date.accessioned | 2004-10-13T00:00:01Z | |
| dc.date.available | 2010-06-18T23:26:06Z | |
| dc.date.issued | 1994-12 | en_US |
| dc.date.submitted | 2004-10-13 | en_US |
| dc.identifier.citation | Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing 1994-12, 9(6):21-27 IEEE Expert | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/105472 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.abstract | 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. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject.other | National Science Digital Library | en_US |
| dc.subject.other | NSDL | en_US |
| dc.subject.other | Artificial Intelligence lab | en_US |
| dc.subject.other | AI lab | en_US |
| dc.subject.other | Machine-learning algorithms | en_US |
| dc.title | Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing | en_US |
| dc.type | Journal Article (Paginated) | en_US |
| dc.identifier.journal | IEEE Expert | en_US |
| refterms.dateFOA | 2018-07-03T01:53:21Z | |
| html.description.abstract | 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. |
