On the Evolution of Planner-Specific Macro Sets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10369850" target="_blank" >RIV/00216208:11320/17:10369850 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-70169-1_33" target="_blank" >http://dx.doi.org/10.1007/978-3-319-70169-1_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-70169-1_33" target="_blank" >10.1007/978-3-319-70169-1_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Evolution of Planner-Specific Macro Sets

Popis výsledku v původním jazyce

In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Most of the macro generation techniques aim for using the same set of generated macros on every problem instance of a given domain. This limits the usefulness of macros in scenarios where the environment and thus the structure of instances is dynamic, such as in real-world applications. Moreover, despite the wide availability of parallel processing units, there is a lack of approaches that can take advantage of multiple parallel cores, while exploiting macros. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues by exploiting multiple cores for combining promising macros --taken from a given pool-- in different sets, while solving continuous streams of problem instances. Our empirical study, involving 5 state-of-the-art planning engines and a large number of planning instances, demonstrates the effectiveness of the proposed MEvo approach.

Název v anglickém jazyce

On the Evolution of Planner-Specific Macro Sets

Popis výsledku anglicky

In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Most of the macro generation techniques aim for using the same set of generated macros on every problem instance of a given domain. This limits the usefulness of macros in scenarios where the environment and thus the structure of instances is dynamic, such as in real-world applications. Moreover, despite the wide availability of parallel processing units, there is a lack of approaches that can take advantage of multiple parallel cores, while exploiting macros. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues by exploiting multiple cores for combining promising macros --taken from a given pool-- in different sets, while solving continuous streams of problem instances. Our empirical study, involving 5 state-of-the-art planning engines and a large number of planning instances, demonstrates the effectiveness of the proposed MEvo approach.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50803 - Information science (social aspects)

Projekt

<a href="/cs/project/GJ17-17125Y" target="_blank" >GJ17-17125Y: Balancování deliberativního a reaktivního chování inteligentních agentů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

AI*IA 2017 Advances in Artificial Intelligence

ISBN

978-3-319-70169-1

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

12

Strana od-do

443-454

Název nakladatele

Springer International Publishing

Místo vydání

Cham, Švýcarsko

Místo konání akce

Bari, Italie

Datum konání akce

14. 11. 2017

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—