Yet more planning efficiency: Finite-domain state-variable reformulation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10319126" target="_blank" >RIV/00216208:11320/15:10319126 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/0952813X.2014.993504" target="_blank" >http://dx.doi.org/10.1080/0952813X.2014.993504</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/0952813X.2014.993504" target="_blank" >10.1080/0952813X.2014.993504</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Yet more planning efficiency: Finite-domain state-variable reformulation

Popis výsledku v původním jazyce

AI Planning is inherently hard and hence it is desirable to derive as much information as we can from the structure of the planning problem and let this information be exploited by a planner. Many recent planners use the finite-domain state-variable representation of the problem instead of the classical propositional representation. However, most planning problems are still specified in the propositional representation due to the widespread modelling language planning domain definition language and it is hard to generate an efficient state-variable representation from the propositional model. In this article, we investigate various methods for automated generation of efficient state-variable representations from the propositional representation and wepropose a novel approach - constructed as a combination of existing techniques - that utilises the structural information from the goal and the initial state. We perform an exhaustive experimental evaluation of methods, planning systems a

Název v anglickém jazyce

Yet more planning efficiency: Finite-domain state-variable reformulation

Popis výsledku anglicky

AI Planning is inherently hard and hence it is desirable to derive as much information as we can from the structure of the planning problem and let this information be exploited by a planner. Many recent planners use the finite-domain state-variable representation of the problem instead of the classical propositional representation. However, most planning problems are still specified in the propositional representation due to the widespread modelling language planning domain definition language and it is hard to generate an efficient state-variable representation from the propositional model. In this article, we investigate various methods for automated generation of efficient state-variable representations from the propositional representation and wepropose a novel approach - constructed as a combination of existing techniques - that utilises the structural information from the goal and the initial state. We perform an exhaustive experimental evaluation of methods, planning systems a

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

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

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Journal of Experimental and Theoretical Artificial Intelligence

ISSN

0952-813X

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

34

Strana od-do

543-576

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84940580966