Uncertainty in Artificial Intelligence
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Towards The Inductive Acquisition of Temporal Knowledge
Kaihu Chen
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: 37-42
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PDF Link: /papers/86/p37-chen.pdf
AUTHOR = "Kaihu Chen ",
TITLE = "Towards The Inductive Acquisition of Temporal Knowledge",
BOOKTITLE = "Proceedings of the Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1986",
PAGES = "37--42"

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