<div class="csl-bib-body">
<div class="csl-entry">Beiser, A., Hecher, M., Unalan, K., & Woltran, S. (2024). Bypassing the ASP Bottleneck: Hybrid Grounding by Splitting and Rewriting. In <i>Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence</i> (pp. 3250–3258). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/360</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/210248
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dc.description.abstract
Answer Set Programming (ASP) is a key paradigm for problems in artificial intelligence and industrial contexts. In ASP, problems are modeled via a set of rules. Over the time this paradigm grew into a rich language, enabling complex rule types like aggregate expressions. Most practical ASP systems follow a ground-and-solve pattern, where rule schemes are grounded and resulting rules are solved. There, the so-called grounding bottleneck may prevent from solving, due to sheer grounding sizes. Recently body-decoupled grounding (BDG) demonstrated how to reduce grounding sizes by delegating effort to solving. However, BDG provides limited interoperability with traditional grounders and only covers simple rule types. In this work, we establish hybrid grounding — based on a novel splitting theorem that allows us to freely combine BDG with traditional grounders. To mitigate huge groundings in practice, we define rewriting procedures for efficiently deferring grounding effort of aggregates to solving. Our experimental results indicate that this approach is competitive, especially for instances, where traditional grounding fails.
en
dc.description.sponsorship
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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dc.language.iso
en
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dc.subject
Answer Set Programming (ASP)
en
dc.subject
Hybrid Grounding
en
dc.subject
Body-Decoupled Grounding (BDG)
en
dc.title
Bypassing the ASP Bottleneck: Hybrid Grounding by Splitting and Rewriting
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
TU Wien, Austria
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dc.contributor.affiliation
Massachusetts Institute of Technology, United States of America (the)
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dc.contributor.affiliation
TU Wien, Austria
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dc.relation.isbn
978-1-956792-04-1
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dc.description.startpage
3250
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dc.description.endpage
3258
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dc.relation.grantno
ICT19-065
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
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tuw.peerreviewed
true
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tuw.relation.publisher
International Joint Conferences on Artificial Intelligence
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tuw.project.title
Revealing and Utilizing the Hidden Structure for Solving Hard Problems in AI
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tuw.researchTopic.id
I1
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tuw.researchTopic.name
Logic and Computation
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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tuw.publication.orgunit
E056-13 - Fachbereich LogiCS
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tuw.publication.orgunit
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML)
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tuw.publisher.doi
10.24963/ijcai.2024/360
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dc.description.numberOfPages
9
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tuw.author.orcid
0000-0003-1594-8972
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tuw.event.name
33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)
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tuw.event.startdate
03-08-2024
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tuw.event.enddate
09-08-2024
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Jeju
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tuw.event.country
KR
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tuw.event.presenter
Beiser, Alexander
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tuw.event.track
Multi Track
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
80
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wb.sciencebranch.value
20
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item.languageiso639-1
en
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.openairetype
conference paper
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item.grantfulltext
none
-
item.fulltext
no Fulltext
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crisitem.project.funder
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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crisitem.project.grantno
ICT19-065
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crisitem.author.dept
TU Wien
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crisitem.author.dept
Massachusetts Institute of Technology
-
crisitem.author.dept
TU Wien
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence