Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00358916" target="_blank" >RIV/68407700:21230/22:00358916 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0010813900003116" target="_blank" >https://doi.org/10.5220/0010813900003116</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010813900003116" target="_blank" >10.5220/0010813900003116</a>
Alternative languages
Result language
angličtina
Original language name
Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks
Original language description
Automated planning provides a powerful general problem solving tool, however, its need for a model creates a bottleneck that can be an obstacle for using it in real-world settings. In this work we propose to use neural networks, namely Cellular Simultaneous Recurrent Networks (CSRN), to process a planning problem and provide a heuristic value estimate that can more efficiently steer the automated planning algorithms to a solution. Using this particular architecture provides us with a scale-free solution that can be used on any problem domain represented by a planar grid. We train the CSRN architecture on two benchmark domains, provide analysis of its generalizing and scaling abilities. We also integrate the trained network into a planner and compare its performance against commonly used heuristic functions.
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
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 2
ISBN
978-989-758-547-0
ISSN
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e-ISSN
2184-433X
Number of pages
11
Pages from-to
203-213
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
000774441800017