Abstract. We describe the HDL algorithm, which learns HTN domain representations by examining plan traces pro- duced by an expert problem-solver.
Learning HTNs means eliciting the hierarchical structure relating tasks and subtasks from a collection of plan traces. Existing works on learning hierarchical ...
Obtain most relevant basics of the most-commonly known hierarchical planning formalization – HTN planning: understand the core differences to ...
Apr 9, 2024 · We introduce CURRICULAMA, an HTN method learning algorithm that completely automates the learning process. It uses landmark analysis to compose annotated tasks.
To apply hierarchical task network (HTN) plan- ning to real-world planning problems, one needs to encode the HTN schemata and action mod- els beforehand.
A key compo- nent of a HTN planning system is a set of decomposition methods, whereby a high-level task is reduced to a set of ordered lower level subtasks. HTN ...
We describe HDL, an algorithm that learns HTN do- main descriptions by examining plan traces produced by an expert problem-solver. Prior work on learning ...
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What is the HTN planning problem?
What is HTN AI?
Aug 29, 2024 · HTN planning is a powerful approach in Artificial Intelligence (AI) that solves complex planning problems by breaking them down into simpler, more manageable ...
Hierarchical Task Network (HTN) planning usually requires a domain engineer to provide manual input about how to de- compose a planning problem.
Jun 20, 2019 · Abstract: The hierarchical task network (HTN) planning technique is used in a growing number of real-world applications.