On the Evolution of Planner-Specific Macro Sets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315074" target="_blank" >RIV/68407700:21230/17:00315074 - isvavai.cz</a>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
On the Evolution of Planner-Specific Macro Sets
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. 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
AI*IA 2017 Advances in Artificial Intelligence
ISBN
978-3-319-70168-4
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
443-454
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Bari
Event date
Nov 14, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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