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Planning with Critical Section Macros: Theory and Practice

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00363583" target="_blank" >RIV/68407700:21230/22:00363583 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1613/JAIR.1.13269" target="_blank" >https://doi.org/10.1613/JAIR.1.13269</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1613/JAIR.1.13269" target="_blank" >10.1613/JAIR.1.13269</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Planning with Critical Section Macros: Theory and Practice

  • Original language description

    Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing "short-cuts" in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process.In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

  • Name of the periodical

    Journal of Artificial Intelligence Research

  • ISSN

    1076-9757

  • e-ISSN

    1943-5037

  • Volume of the periodical

    74

  • Issue of the periodical within the volume

    June

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    42

  • Pages from-to

    691-732

  • UT code for WoS article

    000810515600002

  • EID of the result in the Scopus database

    2-s2.0-85140311439