Hierarchical Decomposition and Analysis for Generalized Planning
This addresses a fundamental gap in AI planning for researchers and practitioners, enabling more reliable synthesis and learning of generalized plans, though it appears incremental as it builds upon classic graph theory results.
The paper tackles the challenge of analyzing and evaluating generalized plans in AI by developing a new framework and methods for assessing termination and goal-reachability properties, significantly extending the class of plans that can be automatically assessed.
This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of generalized plans has been a longstanding goal in AI, it remains challenging due to fundamental gaps in methods for analyzing the scope and utility of a given generalized plan. This paper addresses these gaps by developing a new conceptual framework along with proof techniques and algorithmic processes for assessing termination and goal-reachability related properties of generalized plans. We build upon classic results from graph theory to decompose generalized plans into smaller components that are then used to derive hierarchical termination arguments. These methods can be used to determine the utility of a given generalized plan, as well as to guide the synthesis and learning processes for generalized plans. We present theoretical as well as empirical results illustrating the scope of this new approach. Our analysis shows that this approach significantly extends the class of generalized plans that can be assessed automatically, thereby reducing barriers in the synthesis and learning of reliable generalized plans.