Hierarchical Finite State Controllers for Generalized Planning
This work addresses the challenge of efficient plan representation in automated planning, offering a modular approach that could benefit AI systems requiring scalable solutions, though it appears incremental in extending existing FSC methods.
The paper tackles the problem of representing generalized plans compactly by introducing hierarchical Finite State Controllers (FSCs) that allow controllers to call others, enabling more compact representations than individual FSCs and modular generation, including recursion.
Finite State Controllers (FSCs) are an effective way to represent sequential plans compactly. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans that solve a range of planning problems from a given domain. In this paper we introduce the concept of {\it hierarchical FSCs} for planning by allowing controllers to call other controllers. We show that hierarchical FSCs can represent generalized plans more compactly than individual FSCs. Moreover, our call mechanism makes it possible to generate hierarchical FSCs in a modular fashion, or even to apply recursion. We also introduce a compilation that enables a classical planner to generate hierarchical FSCs that solve challenging generalized planning problems. The compilation takes as input a set of planning problems from a given domain and outputs a single classical planning problem, whose solution corresponds to a hierarchical FSC.