No One SATPlan Encoding To Rule Them All

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10319144" target="_blank" >RIV/00216208:11320/15:10319144 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.aaai.org/ocs/index.php/SOCS/SOCS15/paper/viewFile/10823/10646" target="_blank" >https://www.aaai.org/ocs/index.php/SOCS/SOCS15/paper/viewFile/10823/10646</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

No One SATPlan Encoding To Rule Them All

Popis výsledku v původním jazyce

Solving planning problems via translation to propositional satisfiability (SAT) is one of the most successful approaches to automated planning. An important aspect of this approach is the encoding, i.e., the construction of a propositional formula from agiven planning problem instance. Numerous encoding schemes have been proposed in the recent years each aiming to outperform the previous encodings on the majority of the benchmark problems. In this paper we take a different approach. Instead of trying to develop a new encoding that is better for all kinds of benchmarks we take recently developed specialized encoding schemes and design a method to automatically select the proper encoding for a given planning problem instance. In the paper we also examine ranking heuristics for the Relaxed Relaxed Exists-Step encoding, which plays an important role in our algorithm. Experiments show that our new approach significantly outperforms the state-of-the-art encoding schemes when compared on the

Název v anglickém jazyce

No One SATPlan Encoding To Rule Them All

Popis výsledku anglicky

Solving planning problems via translation to propositional satisfiability (SAT) is one of the most successful approaches to automated planning. An important aspect of this approach is the encoding, i.e., the construction of a propositional formula from agiven planning problem instance. Numerous encoding schemes have been proposed in the recent years each aiming to outperform the previous encodings on the majority of the benchmark problems. In this paper we take a different approach. Instead of trying to develop a new encoding that is better for all kinds of benchmarks we take recently developed specialized encoding schemes and design a method to automatically select the proper encoding for a given planning problem instance. In the paper we also examine ranking heuristics for the Relaxed Relaxed Exists-Step encoding, which plays an important role in our algorithm. Experiments show that our new approach significantly outperforms the state-of-the-art encoding schemes when compared on the

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA15-19877S" target="_blank" >GA15-19877S: Automatické modelování znalostí a plánů pro autonomní roboty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Eighth International Symposium on Combinatorial Search (SoCS-2015)

ISBN

978-1-57735-732-2

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

146-150

Název nakladatele

Association for the Advancement of Artificial Intelligence

Místo vydání

Neuveden

Místo konání akce

Ein Gedi, Izrael

Datum konání akce

11. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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