MEvo: A Framework for Effective Macro Sets Evolution

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

MEvo: A Framework for Effective Macro Sets Evolution

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2020

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

1362-3079

Volume of the periodical

32

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

685-703

UT code for WoS article

000557475300001

EID of the result in the Scopus database

2-s2.0-85073948172