Constructing symbolic representations for high-level planning

Journal Article

Copyright © 2014, Association for the Advancement of Artificial Intelligence. 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 Authors

Cited Authors

  • Konidaris, G; Kaelbling, LP; Lozano-Perez, T

Published Date

  • January 1, 2014

Published In

  • Proceedings of the National Conference on Artificial Intelligence

Volume / Issue

  • 3 /

Start / End Page

  • 1932 - 1938B

Citation Source

  • Scopus