Automated Acquisition of Control Knowledge for Classical Planners

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10407358" target="_blank" >RIV/00216208:11320/20:10407358 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/20:00341714

Výsledek na webu

<a href="https://doi.org/10.5220/0009175209590966" target="_blank" >https://doi.org/10.5220/0009175209590966</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0009175209590966" target="_blank" >10.5220/0009175209590966</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated Acquisition of Control Knowledge for Classical Planners

Popis výsledku v původním jazyce

Attributed transition-based domain control knowledge (ATB-DCK) has been proposed as a simple way to express expected (desirable) sequences of actions in a plan with constraints going beyond physics of the en- vironment. This knowledge can be compiled to Planning Domain Description Language (PDDL) to enhance an existing planning domain model and hence any classical planner can exploit it. In the paper, we propose a method to automatically acquire this control knowledge from example plans. First, a regular expression rep- resenting provided plans is found. Then, this expression is extended with attributes expressing extra relations among the actions and hence going beyond regular languages. The final expression is then translated, through ATB-DCK, to PDDL to enhance a planning domain model. We will empirically demonstrate that such an enhanced domain model improves efficiency of existing state-of-the-art planning engines.

Název v anglickém jazyce

Automated Acquisition of Control Knowledge for Classical Planners

Popis výsledku anglicky

Attributed transition-based domain control knowledge (ATB-DCK) has been proposed as a simple way to express expected (desirable) sequences of actions in a plan with constraints going beyond physics of the en- vironment. This knowledge can be compiled to Planning Domain Description Language (PDDL) to enhance an existing planning domain model and hence any classical planner can exploit it. In the paper, we propose a method to automatically acquire this control knowledge from example plans. First, a regular expression rep- resenting provided plans is found. Then, this expression is extended with attributes expressing extra relations among the actions and hence going beyond regular languages. The final expression is then translated, through ATB-DCK, to PDDL to enhance a planning domain model. We will empirically demonstrate that such an enhanced domain model improves efficiency of existing state-of-the-art planning engines.

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

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) - Volume 2

ISBN

978-989-758-395-7

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

959-966

Název nakladatele

SCITEPRESS – Science and Technology Publications, Lda.

Místo vydání

SETUBAL

Místo konání akce

Valletta, Malta

Datum konání akce

22. 2. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000570769000109