Hierarchical Task Network (HTN) planning usually requires a domain engineer to provide manual input about how to decompose a planning problem. Even HTN-MAKER, a well-known method-learning algorithm, requires a domain engineer to annotate the tasks with information about what to learn. We introduce CURRICULAMA, an HTN method learning algorithm that completely automates the learning process. It uses landmark analysis to compose annotated tasks and leverages curriculum learning to order the learning of methods from simpler to more complex. This eliminates the need for manual input, resolving a core issue with HTN-MAKER. We prove CURRICULAMA's soundness, and show experimentally that it has a substantially similar convergence rate in learning a complete set of methods to HTN-MAKER.

Hierarchical Task Network (HTN) planning is showing its power in real-world planning. Although domain experts have partial hierarchical domain knowledge, it is time-consuming to specify all HTN methods, leaving them incomplete. On the other hand, traditional HTN learning approaches focus only on declarative goals, omitting the hierarchical domain knowledge. In this paper, we propose a novel learning framework to refine HTN methods via task insertion with completely preserving the original methods. As it is difficult to identify incomplete methods without designating declarative goals for compound tasks, we introduce the notion of prioritized preference to capture the incompleteness possibility of methods. Specifically, the framework first computes the preferred completion profile w.r .t.the prioritized preference to refine the incomplete methods. Then it finds the minimal set of refined methods via a method substitution operation. Experimental analysis demonstrates that our approach is effective, especially in solving new HTN planning instances.

Despite the extensive development of first-principles planning in recent years, planning applications are still primarily developed using knowledge-based planners which can exploit domain-specific heuristics and weaker domain models.  Hierarchical Task Network (HTN) planners capture domain-specific heuristics for more efficient search, accommodate incomplete causal models, and can be used to enforce standard operating procedures.  Unfortunately, we do not have semantics for the methods or tasks that make up HTN models, that help evaluate the correctness of methods, or to build a reliable executive for HTN plans.  This paper fills the gap by providing a well-defined semantics for the methods and plans of SHOP2, a state-of-the-art HTN planner.  The semantics are defined in terms of concurrent golog (ConGolog) and the situation calculus.  We provide a proof of equivalence between the plans generated by SHOP2 and the action sequences of the ConGolog semantics.  We show how the semantics reflects the distinction between plan-time and execution-time, and provide some simple examples showing how the semantics can support method verification.  The semantics provide an implementation-neutral specification for an executive, showing how an executive must treat the plans SHOP2 generates in order to enforce the expected behaviors.  Future directions include automated verification of method specifications, automatically generating plan monitors, and plan revision and repair.