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Authors: Marta Vomlelová 1 ; Jindřich Vodrážka 1 ; Roman Barták 1 and Lukáš Chrpa 2 ; 1

Affiliations: 1 Faculty of Mathematics and Physics, Charles University, Czech Republic ; 2 Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic

Keyword(s): Automated Planning, Control Knowledge, Acquisition, Finite State Automata, PDDL.

Abstract: 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 environment. 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 representing 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.

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Paper citation in several formats:
Vomlelová, M.; Vodrážka, J.; Barták, R. and Chrpa, L. (2020). Automated Acquisition of Control Knowledge for Classical Planners. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 959-966. DOI: 10.5220/0009175209590966

@conference{icaart20,
author={Marta Vomlelová. and Jind\v{r}ich Vodrážka. and Roman Barták. and Lukáš Chrpa.},
title={Automated Acquisition of Control Knowledge for Classical Planners},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2020},
pages={959-966},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009175209590966},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Automated Acquisition of Control Knowledge for Classical Planners
SN - 978-989-758-395-7
IS - 2184-433X
AU - Vomlelová, M.
AU - Vodrážka, J.
AU - Barták, R.
AU - Chrpa, L.
PY - 2020
SP - 959
EP - 966
DO - 10.5220/0009175209590966
PB - SciTePress

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