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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

—

Project

<a href="/en/project/GA15-19877S" target="_blank" >GA15-19877S: Automated Knowledge and Plan Modeling for Autonomous Robots</a><br>

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2015

Confidentiality

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

Name of the periodical

Journal of Experimental and Theoretical Artificial Intelligence

ISSN

0952-813X

e-ISSN

—

Volume of the periodical

27

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

34

Pages from-to

543-576

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-84940580966