<div class="csl-bib-body">
<div class="csl-entry">Kiesel, R., & Eiter, T. (2023). Knowledge Compilation and More with SharpSAT-TD. In P. Marquis, T. C. Son, & G. Kern-Isberner (Eds.), <i>Proceedings of the 20th IInternational Conference on Principles of Knowledge Representation and Reasoning</i> (pp. 406–416). IJCAI Organization. https://doi.org/10.24963/kr.2023/40</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/193575
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dc.description.abstract
SharpSAT-TD is a recently published exact model counter that performed exceptionally well in the recent editions of the Model Counting Competition (https://mccompetition.org/). Notably, it additionally features *weighted* model counting capabilities over any semiring. In this work, we show how to exploit this fact to use SharpSAT-TD as a knowledge compiler to the class of sd-DNNF circuits. Our experimental evaluation shows that the efficiency of SharpSAT-TD for (weighted) model counting transfers to knowledge compilation, since it outperforms other state of the art knowledge compilers on standard benchmark sets. Additionally, we generalized SharpSAT-TD's preprocessing to support arbitrary semirings and consider the utility of auxiliary variables in this setting.
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.language.iso
en
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dc.relation.ispartofseries
Proceedings (International Conference on Principles of Knowledge Representation and Reasoning)
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Knowledge Representation and Reasoning
en
dc.subject
Applications of KR
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dc.subject
Probabilistic reasoning and learning
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dc.subject
KR related tools and systems
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dc.title
Knowledge Compilation and More with SharpSAT-TD
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.rights.license
Urheberrechtsschutz
de
dc.rights.license
In Copyright
en
dc.relation.publication
Proceedings of the 20th IInternational Conference on Principles of Knowledge Representation and Reasoning
