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

Persistent Link:
http://hdl.handle.net/10150/105472
Title:
Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing
Author:
Chen, Hsinchun; Buntin, P.; She, Linlin; Sutjahjo, S.; Sommer, C.; Neely, D.
Citation:
Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing 1994-12, 9(6):21-27 IEEE Expert
Publisher:
IEEE
Journal:
IEEE Expert
Issue Date:
Dec-1994
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105472
Submitted date:
2004-10-13
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.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Machine-learning algorithms

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorBuntin, P.en_US
dc.contributor.authorShe, Linlinen_US
dc.contributor.authorSutjahjo, S.en_US
dc.contributor.authorSommer, C.en_US
dc.contributor.authorNeely, D.en_US
dc.date.accessioned2004-10-13T00:00:01Z-
dc.date.available2010-06-18T23:26:06Z-
dc.date.issued1994-12en_US
dc.date.submitted2004-10-13en_US
dc.identifier.citationExpert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing 1994-12, 9(6):21-27 IEEE Experten_US
dc.identifier.urihttp://hdl.handle.net/10150/105472-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractFor 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectArtificial Intelligenceen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherMachine-learning algorithmsen_US
dc.titleExpert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racingen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalIEEE Experten_US
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