Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
A Standard Approach for Optimizing Belief Network Inference using Query DAGs
Adnan Darwiche, Gregory Provan
Abstract:
This paper proposes a novel, algorithm-independent approach to optimizing belief network inference. rather than designing optimizations on an algorithm by algorithm basis, we argue that one should use an unoptimized algorithm to generate a Q-DAG, a compiled graphical representation of the belief network, and then optimize the Q-DAG and its evaluator instead. We present a set of Q-DAG optimizations that supplant optimizations designed for traditional inference algorithms, including zero compression, network pruning and caching. We show that our Q-DAG optimizations require time linear in the Q-DAG size, and significantly simplify the process of designing algorithms for optimizing belief network inference.
Keywords:
Pages: 116-123
PS Link:
PDF Link: /papers/97/p116-darwiche.pdf
BibTex:
@INPROCEEDINGS{Darwiche97,
AUTHOR = "Adnan Darwiche and Gregory Provan",
TITLE = "A Standard Approach for Optimizing Belief Network Inference using Query DAGs",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "116--123"
}


hosted by DSL   •   site info   •   help