Uncertainty in Artificial Intelligence
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Time-Critical Dynamic Decision Making
Yanping Xiang, Kim-Leng Poh
Abstract:
Recent interests in dynamic decision modeling have led to the development of several representation and inference methods. These methods however, have limited application under time critical conditions where a trade-off between model quality and computational tractability is essential. This paper presents an approach to time-critical dynamic decision modeling. A knowledge representation and modeling method called the time-critical dynamic influence diagram is proposed. The formalism has two forms. The condensed form is used for modeling and model abstraction, while the deployed form which can be converted from the condensed form is used for inference purposes. The proposed approach has the ability to represent space-temporal abstraction within the model. A knowledge-based meta-reasoning approach is proposed for the purpose of selecting the best abstracted model that provide the optimal trade-off between model quality and model tractability. An outline of the knowledge-based model construction algorithm is also provided.
Keywords:
Pages: 688-695
PS Link:
PDF Link: /papers/99/p688-xiang.pdf
BibTex:
@INPROCEEDINGS{Xiang99,
AUTHOR = "Yanping Xiang and Kim-Leng Poh",
TITLE = "Time-Critical Dynamic Decision Making",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "688--695"
}


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