Constructing symbolic representations for high-level planning
Publication
, Journal Article
Konidaris, G; Kaelbling, LP; Lozano-Perez, T
Published in: Proceedings of the National Conference on Artificial Intelligence
January 1, 2014
We consider the problem of constructing a symbolic description of a continuous, low-level environment for use in planning. We show that symbols that can represent the preconditions and effects of an agent's actions are both necessary and sufficient for high-level planning. This eliminates the symbol design problem when a representation must be constructed in advance, and in principle enables an agent to autonomously learn its own symbolic representations. The resulting representation can be converted into PDDL, a canonical high-level planning representation that enables very fast planning.
Duke Scholars
Published In
Proceedings of the National Conference on Artificial Intelligence
Publication Date
January 1, 2014
Volume
3
Start / End Page
1932 / 1938B
Citation
APA
Chicago
ICMJE
MLA
NLM
Konidaris, G., Kaelbling, L. P., & Lozano-Perez, T. (2014). Constructing symbolic representations for high-level planning. Proceedings of the National Conference on Artificial Intelligence, 3, 1932-1938B.
Konidaris, G., L. P. Kaelbling, and T. Lozano-Perez. “Constructing symbolic representations for high-level planning.” Proceedings of the National Conference on Artificial Intelligence 3 (January 1, 2014): 1932-1938B.
Konidaris G, Kaelbling LP, Lozano-Perez T. Constructing symbolic representations for high-level planning. Proceedings of the National Conference on Artificial Intelligence. 2014 Jan 1;3:1932-1938B.
Konidaris, G., et al. “Constructing symbolic representations for high-level planning.” Proceedings of the National Conference on Artificial Intelligence, vol. 3, Jan. 2014, pp. 1932-1938B.
Konidaris G, Kaelbling LP, Lozano-Perez T. Constructing symbolic representations for high-level planning. Proceedings of the National Conference on Artificial Intelligence. 2014 Jan 1;3:1932-1938B.
Published In
Proceedings of the National Conference on Artificial Intelligence
Publication Date
January 1, 2014
Volume
3
Start / End Page
1932 / 1938B