Hierarchical Decomposition and Analysis for Generalized Planning
- URL: http://arxiv.org/abs/2212.02823v2
- Date: Mon, 26 Jun 2023 18:43:18 GMT
- Title: Hierarchical Decomposition and Analysis for Generalized Planning
- Authors: Siddharth Srivastava
- Abstract summary: This paper presents new methods for analyzing and evaluating generalized plans.
We develop a new conceptual framework along with proof techniques and algorithmic processes.
We show that this approach significantly extends the class of generalized plans that can be assessed automatically.
- Score: 26.288236123430117
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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