Determining representativeness of training plans: A case of macro-operators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10389701" target="_blank" >RIV/00216208:11320/18:10389701 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/18:00329346
Result on the web
<a href="http://dx.doi.org/10.1109/ICTAI.2018.00081" target="_blank" >http://dx.doi.org/10.1109/ICTAI.2018.00081</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICTAI.2018.00081" target="_blank" >10.1109/ICTAI.2018.00081</a>
Alternative languages
Result language
angličtina
Original language name
Determining representativeness of training plans: A case of macro-operators
Original language description
Most learning for planning approaches rely on analysis of training plans. This is especially the case for one of the best-known learning approach: the generation of macro-operators (macros). These plans, usually generated from a very limited set of training tasks, must provide a ground to extract useful knowledge that can be fruitfully exploited by planning engines. In that, training tasks have to be representative of the larger class of planning tasks on which planning engines will then be run. A pivotal question is how such a set of training tasks can be selected. To address this question, here we introduce a notion of structural similarity of plans. We conjecture that if a class of planning tasks presents structurally similar plans, then a small subset of these tasks is representative enough to learn the same knowledge (macros) as could be learnt from a larger set of tasks of the same class. We have tested our conjecture by focusing on two state-of-the-art macro generation approaches. Our large empirical analysis considering seven state-of-the-art planners, and fourteen benchmark domains from the International Planning Competition, generally confirms our conjecture which can be exploited for selecting small-yet-informative training sets of tasks.
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
<a href="/en/project/GA18-07252S" target="_blank" >GA18-07252S: MoRePlan: Modeling and Reformulating Planning Problems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference on Tools with Artificial Intelligence, ICTAI 2018
ISBN
978-1-5386-7449-9
ISSN
1082-3409
e-ISSN
neuvedeno
Number of pages
5
Pages from-to
488-492
Publisher name
IEEE
Place of publication
NEW YORK, NY 10017 USA
Event location
Volos, Greece
Event date
Nov 5, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000457750200071