Lifted Fact-Alternating Mutex Groups and Pruned Grounding of Classical Planning Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00341807" target="_blank" >RIV/68407700:21230/20:00341807 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1609/aaai.v34i06.6536" target="_blank" >https://doi.org/10.1609/aaai.v34i06.6536</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1609/aaai.v34i06.6536" target="_blank" >10.1609/aaai.v34i06.6536</a>
Alternative languages
Result language
angličtina
Original language name
Lifted Fact-Alternating Mutex Groups and Pruned Grounding of Classical Planning Problems
Original language description
In this paper, we focus on the inference of mutex groups in the lifted (PDDL) representation. We formalize the inference and prove that the most commonly used translator from the Fast Downward (FD) planning system infers a certain subclass of mutex groups, called fact-alternating mutex groups (fam-groups). Based on that, we show that the previously proposed fam-groups-based pruning techniques for the STRIPS representation can be utilized during the grounding process with lifted fam-groups, i.e., before the full STRIPS representation is known. Furthermore, we propose an improved inference algorithm for lifted fam-groups that produces a richer set of fam-groups than the FD translator and we demonstrate a positive impact on the number of pruned operators and overall coverage.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the AAAI Conference on Artificial Intelligence
ISBN
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ISSN
2159-5399
e-ISSN
2374-3468
Number of pages
8
Pages from-to
9835-9842
Publisher name
AAAI Press
Place of publication
Menlo Park
Event location
New York
Event date
Feb 7, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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