LOUGA: learning planning operators using genetic algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10391932" target="_blank" >RIV/00216208:11320/18:10391932 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-319-97289-3_10" target="_blank" >https://doi.org/10.1007/978-3-319-97289-3_10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-97289-3_10" target="_blank" >10.1007/978-3-319-97289-3_10</a>
Alternative languages
Result language
angličtina
Original language name
LOUGA: learning planning operators using genetic algorithms
Original language description
Planning domain models are critical input to current automated planners. These models provide description of planning operators that formalize how an agent can change the state of the world. It is not easy to obtain accurate description of planning operators, namely to ensure that all preconditions and effects are properly specified. Therefore automated techniques to learn them are important for domain modelling. In this paper, we propose a novel method for learning planning operators (action schemata) from example plans. This method, called LOUGA (Learning Operators Using Genetic Algorithms), uses a genetic algorithm to learn action effects and an ad-hoc algorithm to learn action preconditions. We show experimentally that LOUGA is more accurate and faster than the ARMS system, currently the only technique for solving the same type of problem.
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
Knowledge Management and Acquisition for Intelligent Systems. PKAW 2018
ISBN
978-3-319-97288-6
ISSN
0302-9743
e-ISSN
neuvedeno
Number of pages
15
Pages from-to
124-138
Publisher name
Springer
Place of publication
Cham
Event location
Nanjing, China
Event date
Aug 28, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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