Uncertainty in Artificial Intelligence
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Corporate Evidential Decision Making in Performance Prediction Domains
Alex Buchner, Werner Dubitzky, Alfons Schuster, Philippe Lopes, Peter O'Donoghue, John Hughes, David Bell, Kenny Adamson, John White, John Anderson, Maurice Mulvenna
Performance prediction or forecasting sporting outcomes involves a great deal of insight into the particular area one is dealing with, and a considerable amount of intuition about the factors that bear on such outcomes and performances. The mathematical Theory of Evidence offers representation formalisms which grant experts a high degree of freedom when expressing their subjective beliefs in the context of decision-making situations like performance prediction. Furthermore, this reasoning framework incorporates a powerful mechanism to systematically pool the decisions made by individual subject matter experts. The idea behind such a combination of knowledge is to improve the competence (quality) of the overall decision-making process. This paper reports on a performance prediction experiment carried out during the European Football Championship in 1996. Relying on the knowledge of four predictors, Evidence Theory was used to forecast the final scores of all 31 matches. The results of this empirical study are very encouraging.
Keywords: Mathematical theory of evidence, knowledge-based Systems, performance prediction, unc
Pages: 38-45
PS Link: http://www.infj.ulst.ac.uk/~cbgv24/euro96/uai97/uai97.ps
PDF Link: /papers/97/p38-buchner.pdf
AUTHOR = "Alex Buchner and Werner Dubitzky and Alfons Schuster and Philippe Lopes and Peter O'Donoghue and John Hughes and David Bell and Kenny Adamson and John White and John Anderson and Maurice Mulvenna",
TITLE = "Corporate Evidential Decision Making in Performance Prediction Domains",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "38--45"

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