Grid Representation in Neural Networks for Automated Planning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00358914" target="_blank" >RIV/68407700:21230/22:00358914 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0010918500003116" target="_blank" >https://doi.org/10.5220/0010918500003116</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010918500003116" target="_blank" >10.5220/0010918500003116</a>
Alternative languages
Result language
angličtina
Original language name
Grid Representation in Neural Networks for Automated Planning
Original language description
Automated planning and machine learning create a powerful combination of tools which allows us to apply general problem solving techniques to problems that are not modeled using classical planning techniques. In real-world scenarios and complex domains, creating a standardized representation is often a bottleneck as it has to be modeled by a human. That often limits the usage of planning algorithms to real-world problems. The standardized representation is also not a suitable for neural network processing and often requires further transformation. In this work, we focus on presenting three different grid representations that are well suited to model a variety of classical planning problems which can be then processed by neural networks without further modifications. We also analyze classical planning benchmarks in order to find domains that correspond to our proposed representations. Furthermore, we also show that domains that are not explicitly defined on a grid can be represented on a grid with minor modifications that are domain specific. We discuss advantages and drawbacks of our proposed representations, provide examples for many planning benchmarks and also discuss the importance of data and its structure when training neural networks for planning.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
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)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3
ISBN
978-989-758-547-0
ISSN
—
e-ISSN
2184-433X
Number of pages
10
Pages from-to
871-880
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Porto
Event location
Online Streaming
Event date
Mar 3, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000774776400106