Compressing Exact Cover Problems with Zero-suppressed Binary Decision Diagrams
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 1996-2004.
https://doi.org/10.24963/ijcai.2021/275
Keywords
- Knowledge Representation and Reasoning: Knowledge Representation Languages
- Heuristic Search and Game Playing: Combinatorial Search and Optimisation
Abstract
Exact cover refers to the problem of finding subfamily
F of a given family of sets S whose universe
is D, where F forms a partition of D. Knuth’s Algorithm
DLX is a state-of-the-art method for solving
exact cover problems. Since DLX’s running
time depends on the cardinality of input S, it can be
slow if S is large. Our proposal can improve DLX
by exploiting a novel data structure, DanceDD,
which extends the zero-suppressed binary decision
diagram (ZDD) by adding links to enable efficient
modifications of the data structure. With DanceDD,
we can represent S in a compressed way and perform
search in linear time with the size of the structure
by using link operations. The experimental results
show that our method is an order of magnitude
faster when the problem is highly compressed.
@inproceedings{ijcai2021p275,
title = {Compressing Exact Cover Problems with Zero-suppressed Binary Decision Diagrams},
author = {Nishino, Masaaki and Yasuda, Norihito and Nakamura, Kengo},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {1996--2004},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/275},
url = {https://doi.org/10.24963/ijcai.2021/275},
}
TY - CPAPER TI - Compressing Exact Cover Problems with Zero-suppressed Binary Decision Diagrams AU - Nishino, Masaaki AU - Yasuda, Norihito AU - Nakamura, Kengo T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 1996 EP - 2004 DO - 10.24963/ijcai.2021/275 UR - https://doi.org/10.24963/ijcai.2021/275 ER -Copied
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