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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