HyperTensioN and Total-order Forward Decomposition optimizations

Maurício Cecílio Magnaguagno; Felipe Meneguzzi; Lavindra de Silva

HyperTensioN and Total-order Forward Decomposition optimizations

Maurício Cecílio Magnaguagno, Felipe Meneguzzi, Lavindra de Silva

University of Cambridge

Research output: Working paper › Preprint

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Abstract

Hierarchical Task Networks (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. While domain experts develop HTN descriptions, they may repeatedly describe the same preconditions, or methods that are rarely used or possible to be decomposed. By leveraging a three-stage compiler design we can easily support more language descriptions and preprocessing optimizations that when chained can greatly improve runtime efficiency in such domains. In this paper we evaluate such optimizations with the HyperTensioN HTN planner, used in the HTN IPC 2020.

Original language	English
Publisher	ArXiv
Number of pages	9
Publication status	Published - 1 Jul 2022

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HyperTensioN and Total-order Forward Decomposition optimizations

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