Relaxation Heuristics for Multiagent Planning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00224542" target="_blank" >RIV/68407700:21230/14:00224542 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7930" target="_blank" >http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7930</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Relaxation Heuristics for Multiagent Planning

Popis výsledku v původním jazyce

Similarly to classical planning, in MA-Strips multiagent planning, heuristics significantly improve efficiency of search-based planners. Heuristics based on solving a relaxation of the original planning problem are intensively studied and well understood. In particular, frequently used is the delete relaxation, where all delete effects of actions are omitted. In this paper, we present a unified view on distribution of delete relaxation heuristics for multiagent planning. Until recently, the most commonapproach to adaptation of heuristics for multiagent planning was to compute the heuristic estimate using only a projection of the problem for a single agent. In this paper, we place such approach in the context of techniques which allow sharing more information among the agents and thus improve the heuristic estimates. We thoroughly experimentally evaluate properties of our distribution of additive, max and Fast-Forward relaxation heuristics in a planner based on distributed Best-First S

Název v anglickém jazyce

Relaxation Heuristics for Multiagent Planning

Popis výsledku anglicky

Similarly to classical planning, in MA-Strips multiagent planning, heuristics significantly improve efficiency of search-based planners. Heuristics based on solving a relaxation of the original planning problem are intensively studied and well understood. In particular, frequently used is the delete relaxation, where all delete effects of actions are omitted. In this paper, we present a unified view on distribution of delete relaxation heuristics for multiagent planning. Until recently, the most commonapproach to adaptation of heuristics for multiagent planning was to compute the heuristic estimate using only a projection of the problem for a single agent. In this paper, we place such approach in the context of techniques which allow sharing more information among the agents and thus improve the heuristic estimates. We thoroughly experimentally evaluate properties of our distribution of additive, max and Fast-Forward relaxation heuristics in a planner based on distributed Best-First S

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-22125S" target="_blank" >GA13-22125S: Deterministické doménově nezávislé multi-agentní plánování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling

ISBN

978-1-57735-660-8

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

298-306

Název nakladatele

AAAI Press

Místo vydání

Menlo Park, California

Místo konání akce

Portsmouth, NH

Datum konání akce

21. 6. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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