Recursive Reductions of Internal Dependencies in Multiagent Planning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F16%3A00302386" target="_blank" >RIV/68407700:21730/16:00302386 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/16:00302386

Výsledek na webu

<a href="http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220%2f0005754901810191" target="_blank" >http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220%2f0005754901810191</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Recursive Reductions of Internal Dependencies in Multiagent Planning

Popis výsledku v původním jazyce

Problems of cooperative multiagent planning in deterministic environments can be efficiently solved both by distributed search or coordination of local plans. In the current coordination approaches, behavior of other agents is modeled as public external projections of their actions. The agent does not require any additional information from the other agents, that is the planning process ignores any dependencies of the projected actions possibly caused by sequences of other agents’ private actions. In this work, we formally define several types of internal dependencies of multiagent planning problems and provide an algorithmic approach how to extract the internally dependent actions during multiagent planning. We show how to take an advantage of the computed dependencies by means of reducing the multiagent planning problems. We experimentally show strong reduction of majority of standard multiagent benchmarks and nearly doubling of solved problems in comparison to a variant of a planne.

Název v anglickém jazyce

Recursive Reductions of Internal Dependencies in Multiagent Planning

Popis výsledku anglicky

Problems of cooperative multiagent planning in deterministic environments can be efficiently solved both by distributed search or coordination of local plans. In the current coordination approaches, behavior of other agents is modeled as public external projections of their actions. The agent does not require any additional information from the other agents, that is the planning process ignores any dependencies of the projected actions possibly caused by sequences of other agents’ private actions. In this work, we formally define several types of internal dependencies of multiagent planning problems and provide an algorithmic approach how to extract the internally dependent actions during multiagent planning. We show how to take an advantage of the computed dependencies by means of reducing the multiagent planning problems. We experimentally show strong reduction of majority of standard multiagent benchmarks and nearly doubling of solved problems in comparison to a variant of a planne.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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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

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

ISBN

978-989-758-172-4

ISSN

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e-ISSN

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Počet stran výsledku

11

Strana od-do

181-191

Název nakladatele

SciTePress - Science and Technology Publications

Místo vydání

Porto

Místo konání akce

Rome

Datum konání akce

24. 2. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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