Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00360160" target="_blank" >RIV/68407700:21230/22:00360160 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/22:00360160

Výsledek na webu

<a href="https://doi.org/10.1609/aaai.v36i9.21205" target="_blank" >https://doi.org/10.1609/aaai.v36i9.21205</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/aaai.v36i9.21205" target="_blank" >10.1609/aaai.v36i9.21205</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation

Popis výsledku v původním jazyce

Effective decision making while competing for limited resources in adversarial environments is important for many real-world applications (e.g. two Taxi companies competing for customers). Decision-making techniques such as Automated planning have to take into account possible actions of adversary (or competing) agents. That said, the agent should know what the competitor will likely do and then generate its plan accordingly. In this paper we propose a novel approach for estimating strategies of the adversary (or the competitor), sampling its actions that might hinder agent's goals by interfering with the agent's actions. The estimated competitor strategies are used in plan generation such that agent's actions have to be applied prior to the ones of the competitor, whose estimated times dictate the deadlines. We empirically evaluate our approach leveraging sampling of competitor's actions by comparing it to the naive approach optimizing the make-span (not taking the competing agent into account at all) and to Nash Equilibrium (mixed) strategies.

Název v anglickém jazyce

Competing for Resources: Estimating Adversary Strategy for Effective Plan Generation

Popis výsledku anglicky

Effective decision making while competing for limited resources in adversarial environments is important for many real-world applications (e.g. two Taxi companies competing for customers). Decision-making techniques such as Automated planning have to take into account possible actions of adversary (or competing) agents. That said, the agent should know what the competitor will likely do and then generate its plan accordingly. In this paper we propose a novel approach for estimating strategies of the adversary (or the competitor), sampling its actions that might hinder agent's goals by interfering with the agent's actions. The estimated competitor strategies are used in plan generation such that agent's actions have to be applied prior to the ones of the competitor, whose estimated times dictate the deadlines. We empirically evaluate our approach leveraging sampling of competitor's actions by comparing it to the naive approach optimizing the make-span (not taking the competing agent into account at all) and to Nash Equilibrium (mixed) strategies.

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

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

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

Proceedings of the 36th AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-876-3

ISSN

2159-5399

e-ISSN

2374-3468

Počet stran výsledku

9

Strana od-do

9707-9715

Název nakladatele

AAAI Press

Místo vydání

Menlo Park

Místo konání akce

- virtual

Datum konání akce

22. 2. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000893639102081