Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Learning Heuristic Estimates for Planning in Grid Domains by Cellular Simultaneous Recurrent Networks

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2

ISBN

978-989-758-547-0

ISSN

—

e-ISSN

2184-433X

Počet stran výsledku

11

Strana od-do

203-213

Název nakladatele

SciTePress - Science and Technology Publications

Místo vydání

Porto

Místo konání akce

Online Streaming

Datum konání akce

3. 3. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000774441800017