Combining Learning Techniques for Classical Planning: Macro operators and Entanglements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00173971" target="_blank" >RIV/68407700:21230/10:00173971 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Combining Learning Techniques for Classical Planning: Macro operators and Entanglements

Popis výsledku v původním jazyce

Planning techniques recorded a significant progress during recent years. However, many planning problems remain still hard even for modern planners. One of the most promising approaches is gathering additional knowledge by using learning techniques. Wellknown sort of knowledge - macro-operators, formalized like `normal` planning operators, represent a sequence of primitive planning operators. The other sort of knowledge consists of pruning unnecessary operators' instances (actions) by investigating connections (entanglements) between operators and initial or goal predicates. Advantageously, macro-operators and entanglements can be encoded directly in planning domains (or problems) and common planning systems can be applied on them. In this paper, we will show how we can put these approaches together. We will provide an experimental evaluation showing that combining these learning techniques can improve the planning process.

Název v anglickém jazyce

Combining Learning Techniques for Classical Planning: Macro operators and Entanglements

Popis výsledku anglicky

Planning techniques recorded a significant progress during recent years. However, many planning problems remain still hard even for modern planners. One of the most promising approaches is gathering additional knowledge by using learning techniques. Wellknown sort of knowledge - macro-operators, formalized like `normal` planning operators, represent a sequence of primitive planning operators. The other sort of knowledge consists of pruning unnecessary operators' instances (actions) by investigating connections (entanglements) between operators and initial or goal predicates. Advantageously, macro-operators and entanglements can be encoded directly in planning domains (or problems) and common planning systems can be applied on them. In this paper, we will show how we can put these approaches together. We will provide an experimental evaluation showing that combining these learning techniques can improve the planning process.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F08%2F0509" target="_blank" >GA201/08/0509: Integrace strojového učení a splňování omezujících podmínek</a><br>

Návaznosti

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

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of The 22nd IEEE International Conference on Tools with Artificial Intelligence

ISBN

978-0-7695-4263-8

ISSN

1082-3409

e-ISSN

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Počet stran výsledku

8

Strana od-do

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Název nakladatele

IEEE Computer Society

Místo vydání

Cannes

Místo konání akce

Arras

Datum konání akce

27. 10. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000287040000013