Incorporating humans into AI planning is an important feature of flexible planning technology. Such human integration allows to incorporate previously unknown constraints, and is also an integral part of automated modeling assistance. As a foundation for integrating user requests, we study the computational complexity of determining the existence of changes to an existing model, such that the resulting model allows for specific user-provided solutions. We are provided with a planning problem modeled either in the classical (non-hierarchical) or hierarchical task network (HTN) planning formalism, as well as with a supposed-to-be solution plan, which is actually not a solution for the current model. Considering changing decomposition methods as well as preconditions and effects of actions, we show that most change requests are NP-complete though some turn out to be tractable.

Several hierarchical planning systems feature a rich level of language features making them capable of expressing real-world problems. One such feature that's used by several current planning systems is causal links, which are used to track search progress. The formalism combining Hierarchical Task Network (HTN) planning with these links known from Partial Order Causal Link (POCL) planning is often referred to as hybrid planning. In this paper we study the computational complexity of such hybrid planning problems. More specifically, we provide missing membership results to existing hardness proofs and thereby provide tight complexity bounds for all known subclasses of hierarchical planning problems. We also re-visit and correct a result from the literature for plan verification showing that it remains NP-complete even in the absence of a task hierarchy.

Designing a planning domain is a difficult task in AI planning. Assisting tools are thus required if we want planning to be used more broadly. In this paper, we are interested in automatically correcting a flawed domain. In particular, we are concerned with the scenario where a domain contradicts a plan that is known to be valid. Our goal is to repair the domain so as to turn the plan into a solution. Specifically, we consider both grounded and lifted representations support for negative preconditions and show how to explore the space of repairs to find the optimal one efficiently. As an evidence of the efficiency of our approach, the experiment results show that all flawed domains except one in the benchmark set can be repaired optimally by our approach within one second.

In this paper, we consider the plan verification problem for totally ordered (TO) HTN planning. The problem is proved to be solvable in polynomial time by recognizing its connection to the membership decision problem for context-free grammars. Currently, most HTN plan verification approaches do not have special treatments for the TO configuration, and the only one features such an optimization still relies on an exhaustive search. Hence, we will develop a new TOHTN plan verification approach in this paper by extending the standard CYK parsing algorithm which acts as the best decision procedure in general.

Linear Temporal Logic (LTL) has been widely employed in various planning formalisms, e.g., in the STRIPS formalism, in order to specify constraints over state trajectories in a planning problem. In this paper, we investigate the expressive power of two planning formalisms in conjunction with LTL that are most commonly seen in non-hierarchical planning and hierarchical planning respectively, namely the STRIPS formalism and the Hierarchical Task Network (HTN) formalism. We do so by interpreting the set of all solutions to a planning problem as a formal language and comparing it with other formal ones, e.g., star-free languages. Our results provide an in-depth insight into the theoretical properties of the investigated planning formalisms and henceforth explore the common structure shared by solutions to planning problems in certain planning formalisms.