Learning Hierarchical Task Models from Input Traces

Abstract

We describe HTN-Maker, an algorithm for learning hierarchical planning knowledge in the form of task-reduction methods for Hierarchical Task Networks (HTNs). HTN-Maker takes as input a set of planning states from a classical planning domain and plans that are applicable to those states, as well as a set of semantically-annotated tasks to be accomplished. The algorithm analyzes this semantic information in order to determine which portion of the input plans accomplishes a particular task and constructs task-reduction methods based on those analyses.We present theoretical results showing that HTN-Maker is sound and complete. Our experiments in five well-known planning domains confirm the theoretical results and demonstrate convergence toward a set of HTN methods that can be used to solve any problem expressible as a classical planning problem in that domain, relative to a set of goal types for which tasks have been defined. In three of the five domains, HTN planning with the learned methods scales much better than a modern classical planner.

Experience-Based Robot Task Learning and Planning with Goal Inference

“…Some works have addressed how to infer a generalized plan which works over all instances of a class of problems by efficiently instantiating plans for given problems (Hu and De Giacomo 2011). Other works have focused on learning Hierarchical Task Networks (HTN) for hierarchically representing planning knowledge about a problem domain (Hogg, Muñoz-Avila, and Kuter 2014). Although these approaches have seldom been used in robotics (Ingrand and Ghallab 2015), they use more expressive representations and more advanced algorithms than those used so far in robot learning from demonstration, thus we see high application potential.…”

A* Heuristic Based on a Hierarchical Space Model Extracted from Game Replays

“…With a few exceptions, there is not much attention given for supporting state search in classical planning by observed plans [16], [17]. More often the observations are applied to plan recognition [18], [19] or player action prediction [20], [21].…”

“…Another approach that is somehow related to our problem is presented in the work of Hogg [17]. The method learns hierarchical planning knowledge to solve tasks in HTN.…”

A review of machine learning for automated planning

“…HTN-Maker (Hogg et al ., 2008) generates an HTN domain model from a STRIPS domain model, a collection of plans p generated by a STRIPS planner, and a collection of annotated tasks . An annotated task is a triple (n, Pre, Effects) where n is a task, Pre is a set of atoms known as the preconditions, and Eff is a set of atoms known as the effects.…”

“…In Hogg et al . (2009), the HTN-Maker algorithm has been adapted to non-deterministic planning domains, where actions may have multiple possible outcomes.…”