HyperTensioN and Total-order Forward Decomposition optimizations

TL;DR

This paper presents HyperTensioN, an HTN planner optimized through a three-stage compiler design that enhances runtime efficiency by supporting more language descriptions and preprocessing techniques.

Contribution

The paper introduces a novel compiler-based optimization framework for HTN planners, improving their efficiency and flexibility in domain modeling.

Findings

Optimizations significantly reduce planning runtime.

Enhanced language support increases modeling flexibility.

Preprocessing techniques improve planner scalability.

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.