HyperTensioN and Total-order Forward Decomposition optimizations

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/30/2011

Task Interaction in an HTN Planner

Hierarchical Task Network (HTN) planning uses task decomposition to plan...
research
06/26/2011

TALplanner in IPC-2002: Extensions and Control Rules

TALplanner is a forward-chaining planner that relies on domain knowledge...
research
07/06/2016

Cost-Optimal Algorithms for Planning with Procedural Control Knowledge

There is an impressive body of work on developing heuristics and other r...
research
09/09/2022

Compiler Testing using Template Java Programs

We present JAttack, a framework that enables template-based testing for ...
research
11/27/2022

Parallel Optimizations for the Hierarchical Poincaré-Steklov Scheme (HPS)

Parallel optimizations for the 2D Hierarchical Poincaré-Steklov (HPS) di...
research
08/08/2017

On-Stack Replacement à la Carte

On-stack replacement (OSR) dynamically transfers execution between diffe...
research
05/27/2011

The Automatic Inference of State Invariants in TIM

As planning is applied to larger and richer domains the effort involved ...

Please sign up or login with your details

Forgot password? Click here to reset