Endomorphisms of Classical Planning Tasks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00350200" target="_blank" >RIV/68407700:21230/21:00350200 - isvavai.cz</a>
Result on the web
<a href="https://ojs.aaai.org/index.php/AAAI/article/view/17406" target="_blank" >https://ojs.aaai.org/index.php/AAAI/article/view/17406</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Endomorphisms of Classical Planning Tasks
Original language description
Detection of redundant operators that can be safely removed from the planning task is an essential technique allowing to greatly improve performance of planners. In this paper, we employ structure-preserving maps on labelled transition systems (LTSs), namely endomorphisms well known from model theory, in order to detect redundancy. Computing endomorphisms of an LTS induced by a planning task is typically infeasible, so we show how to compute some of them on concise representations of planning tasks such as finite domain representations and factored LTSs. We formulate the computation of endomorphisms as a constraint satisfaction problem (CSP) that can be solved by an off-the-shelf CSP solver. Finally, we experimentally verify that the proposed method can find a sizeable number of redundant operators on the standard benchmark set.
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
<a href="/en/project/GJ18-24965Y" target="_blank" >GJ18-24965Y: Privacy Preserving Multi-agent Planning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
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 Thirty-Fifth AAAI Conference on Artificial Intelligence
ISBN
978-1-57735-866-4
ISSN
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e-ISSN
2374-3468
Number of pages
9
Pages from-to
11835-11843
Publisher name
Association for the Advancement of Artificial Intelligence (AAAI)
Place of publication
Palo Alto, California
Event location
Virtual Conference
Event date
Feb 2, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000681269803058