Kiesel, R., & Eiter, T. (2023). Knowledge Compilation and More with SharpSAT-TD. In P. Marquis, T. C. Son, & G. Kern-Isberner (Eds.), Proceedings of the 20th IInternational Conference on Principles of Knowledge Representation and Reasoning (pp. 406–416). IJCAI Organization. https://doi.org/10.24963/kr.2023/40
E192-03 - Forschungsbereich Knowledge Based Systems
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Erschienen in:
Proceedings of 20th International Conference on Principles of Knowledge Representation and Reasoning
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ISBN:
978-1-956792-02-7
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Datum (veröffentlicht):
2023
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Veranstaltungsname:
20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023)
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Veranstaltungszeitraum:
2-Sep-2023 - 8-Sep-2023
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Veranstaltungsort:
Rhodes, Griechenland
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Umfang:
11
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Verlag:
IJCAI Organization
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Peer Reviewed:
Ja
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Keywords:
Knowledge Representation and Reasoning; Applications of KR; Probabilistic reasoning and learning; KR related tools and systems
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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.
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Projekttitel:
Doktoratskolleg: W 1255-N23 (FWF - Österr. Wissenschaftsfonds)
