Uncertainty in Artificial Intelligence
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Some Experiments with Real-Time Decision Algorithms
Bruce D'Ambrosio, Scott Burgess
Abstract:
Real-time Decision algorithms are a class of incremental resource-bounded [Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence diagrams. We present a test domain for real-time decision algorithms, and the results of experiments with several Real-time Decision Algorithms in this domain. The results demonstrate high performance for two algorithms, a decision-evaluation variant of Incremental Probabilisitic Inference [D'Ambrosio 93] and a variant of an algorithm suggested by Goldszmidt, [Goldszmidt, 95], PK-reduced. We discuss the implications of these experimental results and explore the broader applicability of these algorithms.
Keywords: Real-time, influence diagrams, evaluation, SPI.
Pages: 194-202
PS Link: ftp://ftp.engr.orst.edu/pub/dambrosi/uai-96.ps.Z
PDF Link: /papers/96/p194-d_ambrosio.pdf
BibTex:
@INPROCEEDINGS{D'Ambrosio96,
AUTHOR = "Bruce D'Ambrosio and Scott Burgess",
TITLE = "Some Experiments with Real-Time Decision Algorithms",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "194--202"
}


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