MEvo: A Framework for Effective Macro Sets Evolution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00334341" target="_blank" >RIV/68407700:21230/20:00334341 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1080/0952813X.2019.1672796" target="_blank" >https://doi.org/10.1080/0952813X.2019.1672796</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/0952813X.2019.1672796" target="_blank" >10.1080/0952813X.2019.1672796</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MEvo: A Framework for Effective Macro Sets Evolution

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. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide `general improvement’, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper, we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros – taken from a given pool – while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.

Název v anglickém jazyce

MEvo: A Framework for Effective Macro Sets Evolution

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. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide `general improvement’, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper, we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros – taken from a given pool – while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2020

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

1362-3079

Svazek periodika

32

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

685-703

Kód UT WoS článku

000557475300001

EID výsledku v databázi Scopus

2-s2.0-85073948172