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Generalized Planning: Synthesizing Plans that Work for Multiple Environments

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 918-922.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-159

Abstract

We give a formal definition of generalized planning that is independent of any representation formalism. We assume that our generalized plans must work on a set of deterministic environments, which are essentially unrelated to each other. We prove that generalized planning for a finite set of environments is always decidable and EXPSPACE-complete. Our proof is constructive and gives us a sound, complete and complexity-wise optimal technique. We also consider infinite sets of environments, and show that generalized planning for the infinite one-dimensional problems, known in the literature to be recursively enumerable when restricted to finite-state plans, is EXPSPACE-decidable without sequence functions, and solvable by generalized planning for finite sets.

Yuxiao Hu, and Giuseppe De Giacomo. 2011. Generalized Planning: Synthesizing Plans that Work for Multiple Environments. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 918–922. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-159
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