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Adversary Strategy Sampling for Effective Plan Generation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00352524" target="_blank" >RIV/68407700:21230/21:00352524 - isvavai.cz</a>

  • Result on the web

    <a href="https://ojs.aaai.org/index.php/SOCS/article/view/18571" target="_blank" >https://ojs.aaai.org/index.php/SOCS/article/view/18571</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Adversary Strategy Sampling for Effective Plan Generation

  • Original language description

    Effective plan generation in adversarial environments has totake into account possible actions of adversary agents, i.e.,the agent should know what the competitor will likely do.In this paper we propose a novel approach for estimatingstrategies of the adversary, sampling actions that interferewith the agent’s ones. The estimated competitor strategies areused in plan generation by considering that agent’s actionshave to be applied prior to the ones of the competitor, whoseestimated times dictate the agent’s deadlines. Missing thesedeadlines entails additional plan cost

  • 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

    2021

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the International Symposium on Combinatorial Search

  • ISBN

    9781713834557

  • ISSN

  • e-ISSN

  • Number of pages

    3

  • Pages from-to

    164-166

  • Publisher name

    Association for the Advancement of Artificial Intelligence (AAAI)

  • Place of publication

    Palo Alto, California

  • Event location

    Virtual conference

  • Event date

    Jul 26, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article