Uncertainty in Artificial Intelligence
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Learning to Predict: An Inductive Approach
Kaihu Chen
Abstract:
The ability to predict the future in a given domain can be acquired by discovering empirically from experience certain temporal patterns that tend to repeat unerringly. Previous works in time series analysis allow one to make quantitative predictions on the likely values of certain linear variables. Since certain types of knowledge are better expressed in symbolic forms, making qualitative predictions based on symbolic representations require a different approach. A domain independent methodology called TIM (Time based Inductive Machine) for discovering potentially uncertain temporal patterns from real time observations using the technique of inductive inference is described here.
Keywords: TIM, Inductive Inference, Independent Methodology
Pages: 115-123
PS Link:
PDF Link: /papers/86/p115-chen.pdf
BibTex:
@INPROCEEDINGS{Chen86,
AUTHOR = "Kaihu Chen ",
TITLE = "Learning to Predict: An Inductive Approach",
BOOKTITLE = "Uncertainty in Artificial Intelligence 2 Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1986",
PAGES = "115--123"
}


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