Computing programs for generalized planning using a classical planner. Segovia-Aguas, J., Jiménez, S., & Jonsson, A. Artificial Intelligence, 2019. doi abstract bibtex Generalized planning is the task of generating a single solution (a generalized plan) that is valid for multiple planning instances. In this paper we introduce a novel formalism for representing generalized plans that borrows two mechanisms from structured programming: control flow and procedure calls. On one hand, control flow structures allow to compactly represent generalized plans. On the other hand, procedure calls allow to represent hierarchical and recursive solutions as well as to reuse existing generalized plans. The paper also presents a compilation from generalized planning into classical planning which allows us to compute generalized plans with off-the-shelf planners. The compilation can incorporate prior knowledge in the form of auxiliary procedures which expands the applicability of the approach to more challenging tasks. Experiments show that a classical planner using our compilation can compute generalized plans that solve a wide range of generalized planning tasks, including sorting lists of variable size or DFS traversing variable-size binary trees. Additionally the paper presents an extension of the compilation for computing generalized plans when generalization requires a high-level state representation that is not provided a priori. This extension brings a new landscape of benchmarks to classical planning since classification tasks can naturally be modeled as generalized planning tasks, and hence, as classical planning tasks. Finally the paper shows that the compilation can be extended to compute control knowledge for off-the-shelf planners and solve planning instances that are difficult to solve without such additional knowledge.
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title = {Computing programs for generalized planning using a classical planner},
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abstract = {Generalized planning is the task of generating a single solution (a generalized plan) that is valid for multiple planning instances. In this paper we introduce a novel formalism for representing generalized plans that borrows two mechanisms from structured programming: control flow and procedure calls. On one hand, control flow structures allow to compactly represent generalized plans. On the other hand, procedure calls allow to represent hierarchical and recursive solutions as well as to reuse existing generalized plans. The paper also presents a compilation from generalized planning into classical planning which allows us to compute generalized plans with off-the-shelf planners. The compilation can incorporate prior knowledge in the form of auxiliary procedures which expands the applicability of the approach to more challenging tasks. Experiments show that a classical planner using our compilation can compute generalized plans that solve a wide range of generalized planning tasks, including sorting lists of variable size or DFS traversing variable-size binary trees. Additionally the paper presents an extension of the compilation for computing generalized plans when generalization requires a high-level state representation that is not provided a priori. This extension brings a new landscape of benchmarks to classical planning since classification tasks can naturally be modeled as generalized planning tasks, and hence, as classical planning tasks. Finally the paper shows that the compilation can be extended to compute control knowledge for off-the-shelf planners and solve planning instances that are difficult to solve without such additional knowledge.},
bibtype = {article},
author = {Segovia-Aguas, Javier and Jiménez, Sergio and Jonsson, Anders},
doi = {10.1016/j.artint.2018.10.006},
journal = {Artificial Intelligence}
}
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