Uncertainty in Artificial Intelligence
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Hybrid Probabilistic Programs: Algorithms and Complexity
Michael Dekhtyar, Alex Dekhtyar, V. Subrahmanian
Abstract:
Hybrid Probabilistic Programs (HPPs) are logic programs that allow the programmer to explicitly encode his knowledge of the dependencies between events being described in the program. In this paper, we classify HPPs into three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide three types of results for HPPs. First, we develop algorithms to compute the set of all ground consequences of an HPP. Then we provide algorithms and complexity results for the problems of entailment (``Given an HPP P and a query Q as input, is Q a logical consequence of P?'') and consistency (``Given an HPP P as input, is P consistent?''). Our results provide a fine characterization of when polynomial algorithms exist for the above problems, and when these problems become intractable.
Keywords: probabilisitc logic programming, complexity, proof complexity
Pages: 160-169
PS Link: http://www.cs.umd.edu:80/Dienst/UI/2.0/Describe/ncstrl.umcp/CS-TR-3969?abstract=Dekht
PDF Link: /papers/99/p160-dekhtyar.pdf
BibTex:
@INPROCEEDINGS{Dekhtyar99,
AUTHOR = "Michael Dekhtyar and Alex Dekhtyar and V. Subrahmanian",
TITLE = "Hybrid Probabilistic Programs: Algorithms and Complexity",
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 = "160--169"
}


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