Affine Algebraic Decision Diagrams (AADDs) and their Application to Structured Probabilistic Inference
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1384-1389.
Abstract
We propose an affine extension to ADDs (AADD) capable of compactly representing context-specific, additive, and multiplicative structure. We show that the AADD has worstcase time and space performance within a multiplicative constant of that of ADDs, but that it can be linear in the number of variables in cases where ADDs are exponential in the number of variables. We provide an empirical comparison of tabular, ADD, and AADD representations used in standard Bayes net and MDP inference algorithms and conclude that the AADD performs at least as well as the other two representations, and often yields an exponential performance improvement over both when additive or multiplicative structure can be exploited. These results suggest that the AADD is likely to yield exponential time and space improvements for a variety of probabilistic inference algorithms that currently use tables or ADDs.
@inproceedings{ijcai2005p181,
title = {Affine Algebraic Decision Diagrams (AADDs) and their Application to Structured Probabilistic Inference},
author = {Sanner, Scott and McAllester, David A.},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1384--1389},
year = {2005},
url = {https://ijcai.org/proceedings/2005/181},
}
TY - CPAPER TI - Affine Algebraic Decision Diagrams (AADDs) and their Application to Structured Probabilistic Inference AU - Sanner, Scott AU - McAllester, David A. T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1384 EP - 1389 UR - https://ijcai.org/proceedings/2005/181 ER -Copied
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