Domain learning is the task of inferring actions' preconditions and effects (domains) from executed sequences of actions (plans) along with a varying detail of information about the corresponding world states. Remarkably, even if the state remains completely unobserved, as in this work, we can infer the existence of certain state features if we assume that the plans we learn from are non-redundant. Moreover, plans might be redundant regardless of the underlying domain. We study the computational complexity of deciding whether there exists a domain in which a given plan is justified in the sense that either no single action (well-justification) or no set of actions (perfect justification) can be removed without violating correctness of the plan. We allow either arbitrarily large domains or domains with a polynomial bound on the number of state variables. We show that the problem is in P for well-justified plans and arbitrary domains, NP-complete for well-justified plans and bounded domains, in coNP for perfectly justified plans and arbitrary domains, and in Σ₂ for perfectly justified plans and bounded domains.

We introduce a new version of general game-playing in which one of the players chooses the length of the game up front. Consider a classical planning problem and two players who take turns applying actions. Player 1 wins iff the goal is true after a predetermined number of moves has been made. Is there a number r such that player 1 has a winning strategy for the game of length r? We show that this problem is EXPSPACE-complete. Moreover, we show that the problem is equivalent to the plan existence problem for a class of fully observable non-deterministic hierarchical task network planning problems under the solution concept with fixed methods, which was introduced in prior work. This class consists of all regular loop-unrolling problems, where a problem is loop-unrolling if it has at most one compound task name and at most two methods. As a corollary, we obtain hardness for regular problems, solving an open problem.

Domain learning is the task of finding an action model that can explain given observed plan executions, so-called traces. It allows us to automate the identification of actions' preconditions and effects instead of relying on hand-modeled expert knowledge. While previous research has put forth various techniques and covers multiple planning formalisms, the theoretical foundations of domain learning are still in their infancy. We investigate the most basic setting, that is grounded classical planning without negative preconditions or conditional effects with full observability of the state variables. The given traces are assumed to be justified in the sense that either no single action or no set of actions can be removed without violating correctness of the plan. Furthermore, we might be given additional constraints in the form of a propositional logical formula. We show the consequences of these assumptions for the computational complexity of identifying a satisfactory planning domain.

Planning is the act of deliberative thinking before acting. It is based on a symbolic model of the world and the options to act in it, usually defined in function-free first-order logic. The task is to find a sequence of actions (a plan) that leads from a given current state to a desired goal state. The basic, purely physical description may be augmented with a partially ordered grammar-like structure (a Hierarchical Task Network or HTN), which can describe expert knowledge, or practical, legal, or operational requirements. In this talk, I will survey a variety of methods for automatically deriving plans using symbolic methods for planning -- from both my past and future research. These symbolic methods -- in some sense -- translate planning problems into other, simpler symbolic representations and reason over them to find plans. As a basis for these methods, I will firstly introduce relevant theoretical results on planning. First, I will discuss the expressive power of planning formalisms (ECAI'14, ICAPS'16) and second, the computational complexity of HTN planning and related tasks such as HTN plan verification, plan modification, and plan recognition (ICAPS'15, ICAPS'16). Based on these theoretical results, I will develop why SAT-based HTN planning is possible and how it can be implemented. To this end, I will survey several of my publications at top-tier conferences, including papers at ICAPS'17, AAAI'18, AAAI'19, IJCAI'19, AAAI'20, and ICAPS'21 -- in which I developed an highly SAT-based planner for HTN problems including the ability to find optimal plans as well as the grounding as a preprocessing step. Here I will also give an outlook on future developments and new ideas that I propose for SAT-based planning -- including the exploitation of structures in plan (e.g.\ landmarks or operator-counting constraints). Next, I will present the idea of expressing lifted classical planning as SAT (ICAPS'22). The resulting planner LiSAT was the first lifted SAT-based planner -- and proved highly efficient and outperformed all other lifted planners at the time of publication. Notably, LiSAT was the first planner (lifted or grounded) and still is the only one to solve the challenging OrganicSynthesis benchmark -- and could even prove optimality for all plans. I will also outline future ideas to further improve the efficiency of LiSAT. Lastly, I introduce the notion of planning with symbolic symbolic representations (AAAI'21 and ICAPS'23). Here one uses Binary Decision Diagrams to encode large sets of states efficiently. For expressing the additional structure encoded by HTNs, I show how BDDs can be suitably integrated into finite automata. Based on this representation, an efficient and optimal planning algorithm can be derived. Additionally, I show how this algorithm can be extended to also cover oversubscription planning.

The journal track of the 27th European Conference on Artificial Intelligence (ECAI-2024) offered the authors of papers recently accepted for publication by either one of the two leading discipline-wide journals in AI, Artificial Intelligence (AIJ) and the Journal of Artificial Intelligence Research (JAIR), the opportunity to present their work at the conference without undergoing an additional round of reviewing. Papers were eligible only if no part had previously been presented at a conference with archival proceedings. Traditionally, the authors of such papers would have missed out on the opportunity to present their work to a broader research audience. This limitation tends to discourage the submission of original work to journals without prior conference publications on the same topic. The intention of the journal track is to encourage a “journal-first” publication strategy–by giving authors the option to present their work at a suitable conference venue such as ECAI. On the following pages, for each paper presented at the journal track, we list bibliographic information and the abstract of the original publication.

Both numeric planning and Hierarchical Task Network (HTN) planning are highly expressive planning formalisms – at the cost of being undecidable in general. For both formalisms, decidable fragments are known. Studying these restricted fragments has lead to valuable insights, which ultimately gave rise to the development of new efficient planning algorithms. We identify new decidable fragments of both numeric and HTN planning. For HTN planning, we introduce the fragments of one-hole-digging, initial, and final problems. The former restrict every task network to have at most one compound task, while the latter two restrict compound tasks to be order-minimal or order-maximal, respectively. For numeric planning, we introduce Positive Numeric Planning (PNP) where the value of numeric variables can only be non-negative. We determine the complexity of these fragments: they are Ackermann-complete – which is significantly more difficult than any prior known decidable fragment, but still barely decidable.

Stackelberg planning is a recently introduced single-turn two-player adversarial planning model, where two players are acting in a joint classical planning task, the objective of the first player being hampering the second player from achieving its goal. This places the Stackelberg planning problem somewhere between classical planning and general combinatorial two-player games. But, where exactly? All investigations of Stackelberg planning so far focused on practical aspects. We close this gap by conducting the first theoretical complexity analysis of Stackelberg planning. We show that in general Stackelberg planning is actually no harder than classical planning. Under a polynomial plan-length restriction, however, Stackelberg planning is a level higher up in the polynomial complexity hierarchy, suggesting that compilations into classical planning come with a worst-case exponential plan-length increase. In attempts to identify tractable fragments, we further study its complexity under various planning task restrictions, showing that Stackelberg planning remains intractable where classical planning is not. We finally inspect the complexity of meta-operator verification, a problem that has been recently connected to Stackelberg planning.

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.

Plan verification is the task of deciding whether a given plan is a solution to a planning problem. In this paper, we study the plan verification problem in the context of Hierarchical Task Network (HTN) planning, which has been proved to be NP-complete when partial order (PO) is involved. We will develop a novel SAT-based approach exploiting the data structures solution order graphs and path decomposition trees which encodes an HTN plan verification problem as a SAT one. We show in our experiments that this new approach outperforms the current state-of-the-art (SOTA) planning-based approach for verifying plans for POHTN problems.

The problem of hierarchical plan verification focuses on checking whether an action sequence is a valid hierarchical plan the action sequence is executable and a goal task can be decomposed into it. The existing parsing-based verifier works on lifted domain models. In this paper we study whether grounding of the models could improve efficiency of the verifier. We also explore additional implementation improvements to increase the speed of the verifier.

Legal language is considered to be a key obstacle to the comprehensibility of court decisions for laypeople. While differences between written 'standard' and legal language have already been analysed with regard to syntactic peculiarities, there is still a lack of findings on the influence of divergent word meanings on comprehensibility. We present the course and the preliminary results of a study elaborating such ambiguities on the basis of over half a million German court decisions. As these differences are highly language-dependent, our study consequentially relates (only) to German.

Translation-based approaches to planning allow for solving problems in complex and expressive formalisms via the means of highly efficient solvers for simpler formalisms. To be effective, these translations have to be constructed appropriately. The current existing translation of the highly expressive formalism of HTN planning into the more simple formalism of classical planning is not on par with the performance of current dedicated HTN planners. With our contributions in this paper, we close this gap: we describe new versions of the translation that reach the performance of state-of-the-art dedicated HTN planners. We present new translation techniques both for the special case of totally-ordered HTNs as well as for the general partially-ordered case. In the latter, we show that our new translation generates only linearly many actions, while the previous encoding generates and exponential number of actions.

Symbolic search has proven to be a useful approach to optimal classical planning. In Hierarchical Task Network (HTN) planning, however, there is little work on optimal planning. One reason for this is that in HTN planning, most algorithms are based on heuristic search, and admissible heuristics have to incorporate the structure of the task network in order to be informative. In this paper, we present a novel approach to optimal (totally-ordered) HTN planning, which is based on symbolic search. An empirical analysis shows that our symbolic approach outperforms the current state of the art for optimal totally-ordered HTN planning.

Verifying HTN plans is an intractable problem with two existing approaches to solve the problem. One technique is based on compilation to SAT. Another method is using parsing, and it is currently the fastest technique for verifying HTN plans and the only technique supporting state constraints. In this paper, we propose an extension of the parsing-based approach to verify totally-ordered HTN plans more efficiently. This problem is known to be tractable if no state constraints are included, and we show theoretically and empirically that the modified parsing approach achieves better performance than the currently fastest HTN plan verifier when applied to totally-ordered HTN plans.

Hierarchical task network (HTN) planning is a model-based approach to planning. The HTN domain model consists of tasks and methods to decompose them into subtasks until obtaining primitive tasks (actions). There are recent methods for verifying if a given action sequence is a valid HTN plan. However, if the plan is invalid, all existing verification methods only say so without explaining why the plan is invalid. In the paper, we propose a method that corrects a given action sequence to form a valid HTN plan by deleting the minimal number of actions. This plan correction explains what is wrong with a given action sequence concerning the HTN domain model.

Both search-based and translation-based planning systems usually operate on grounded representations of the problem. Planning models, however, are commonly defined using lifted description languages. Thus, planning systems usually generate a grounded representation of the lifted model as a preprocessing step. For HTN planning models, only one method to ground lifted models has been published so far. In this paper we present a new approach for grounding HTN planning problems that produces smaller groundings in a shorter timespan than the previously published method.

The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and – much more important – also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems.

Effectively supporting novices during performance of complex tasks, e.g. do-it-yourself (DIY) projects, requires intelligent assistants to be more than mere instructors. In order to be accepted as a competent and trustworthy cooperation partner, they need to be able to actively participate in the project and engage in helpful conversations with users when assistance is necessary. Therefore, a new proactive version of the DIY-assistant Robert is presented in this paper. It extends the previous prototype by including the capability to initiate reflective meta-dialogues using multimodal cues. Two different strategies for reflective dialogue are implemented: A progress-based strategy initiates a reflective dialogue about previous experience with the assistance for encouraging the self-appraisal of the user. An activity-based strategy is applied for providing timely, task-dependent support. Therefore, user activities with a connected drill driver are tracked that trigger dialogues in order to reflect on the current task and to prevent task failure. An experimental study comparing the proactive assistant against the baseline version shows that proactive meta-dialogue is able to build user trust significantly better than a solely reactive system. Besides, the results provide interesting insights for the development of proactive dialogue assistants.

Hierarchical Task Networks were proposed as a method to describe plans by decomposition of tasks to subtasks until primitive tasks, actions, are obtained. Valid plans - sequences of actions - must adhere both to causal dependencies between the actions and to the structure given by the decomposition of the goal task. Plan verification aims at finding if a given plan is valid, that is, if it is causally consistent and it can be obtained by decomposition of some task. The paper describes a novel parsing-based approach for hierarchical plan verification that is orders of magnitude faster than existing methods.

In HTN planning, the hierarchy has a wide impact on solutions. First, there is (usually) no state-based goal given, the objective is given via the hierarchy. Second, it enforces actions to be in a plan. Third, planners are not allowed to add actions apart from those introduced via decomposition, i.e. via the hierarchy. However, no heuristic considers the interplay of hierarchy and actions in the plan exactly (without relaxation) because this makes heuristic calculation NP-hard even under delete relaxation. We introduce the problem class of delete- and ordering-free HTN planning as basis for novel HTN heuristics and show that its plan existence problem is still NP-complete. We then introduce heuristics based on the new class using an integer programming model to solve it.

The majority of search-based HTN planning systems can be divided into those searching a space of partial plans (a plan space) and those performing progression search, i.e., that build the solution in a forward manner. So far, all HTN planners that guide the search by using heuristic functions are based on plan space search. Those systems represent the set of search nodes more effectively by maintaining a partial ordering between tasks, but they have only limited information about the current state during search. In this article, we propose the use of progression search as basis for heuristic HTN planning systems. Such systems can calculate their heuristics incorporating the current state, because it is tracked during search. Our contribution is the following: We introduce two novel progression algorithms that avoid unnecessary branching when the problem at hand is partially ordered and show that both are sound and complete. We show that defining systematicity is problematic for search in HTN planning, propose a definition, and show that it is fulfilled by one of our algorithms. Then, we introduce a method to apply arbitrary classical planning heuristics to guide the search in HTN planning. It relaxes the HTN planning model to a classical model that is only used for calculating heuristics. It is updated during search and used to create heuristic values that are used to guide the HTN search. We show that it can be used to create HTN heuristics with interesting theoretical properties like safety, goal-awareness, and admissibility. Our empirical evaluation shows that the resulting system outperforms the state of the art in search-based HTN planning.

HTN planning provides an expressive formalism to model complex application domains. It has been widely used in realworld applications. However, the development of domainindependent planning techniques for such models is still lacking behind. The need to be informed about both statetransitions and the task hierarchy makes the realisation of search-based approaches difficult, especially with unrestricted partial ordering of tasks in HTN domains. Recently, a translation of HTN planning problems into propositional logic has shown promising empirical results. Such planners benefit from a unified representation of state and hierarchy, but until now require very large formulae to represent partial order. In this paper, we introduce a novel encoding of HTN Planning as SAT. In contrast to related work, most of the reasoning on ordering relations is not left to the SAT solver, but done beforehand. This results in much smaller formulae and, as shown in our evaluation, in a planner that outperforms previous SAT-based approaches as well as the state-of-the-art in search-based HTN planning.

Over the last years, several new approaches to Hierarchical Task Network (HTN) planning have been proposed that increased the overall performance of HTN planners. However, the focus has been on agile planning - on finding a solution as quickly as possible. Little work has been done on finding optimal plans. We show how the currently best-performing approach to HTN planning - the translation into propositional logic - can be utilised to find optimal plans. Such SAT-based planners usually bound the HTN problem to a certain depth of decomposition and then translate the problem into a propositional formula. To generate optimal plans, the length of the solution has to be bounded instead of the decomposition depth. We show the relationship between these bounds and how it can be handled algorithmically. Based on this, we propose an optimal SAT-based HTN planner and show that it performs favourably on a benchmark set.

Planning is the task of finding a sequence of actions that achieves the goal(s) of an agent. It is solved based on a model describing the environment and how to change it. There are several approaches to solve planning tasks, two of the most popular are classical planning and hierarchical planning. Solvers are often based on heuristic search, but especially regarding domain-independent heuristics, techniques in classical planning are more sophisticated. However, due to the different problem classes, it is difficult to use them in hierarchical planning. In this paper we describe how to use arbitrary classical heuristics in hierarchical planning and show that the resulting system outperforms the state of the art in hierarchical planning.

In this paper, we propose a novel SAT-based planning approach for hierarchical planning by introducing the SAT-based planner totSAT for the class of totally-ordered HTN planning problems. We use the same general approach as SAT planning for classical planning does: bound the problem, translate the problem into a formula, and if the formula is not satisfiable, increase the bound. In HTN planning, a suitable bound is the maximum depth of decomposition. We show how totally-ordered HTN planning problems can be translated into a SAT formula, given this bound. Furthermore, we have conducted an extensive empirical evaluation to compare our new planner against state-of-the-art HTN planners. It shows that our technique outperforms any of these systems.

Planning via SAT has proven to be an efficient and versatile planning technique. Its declarative nature allows for an easy integration of additional constraints and can harness the progress made in the SAT community without the need to adapt the planner. However, there has been only little attention to SAT planning for hierarchical domains. To ease encoding, existing approaches for HTN planning require additional assumptions, like non-recursiveness or totally-ordered methods. Both limit the expressiveness of HTN planning severely. We propose the first propositional encodings which are able to solve general, i.e., partially-ordered, HTN planning problems, based on a previous encoding for totally-ordered problems. The empirical evaluation of our encoding shows that it outperforms existing HTN planners significantly.

Plan-and Goal Recognition (PGR) is the task of inferring the goals and plans of an agent based on its actions. Traditional approaches in PGR are based on a plan library including pairs of plans and corresponding goals. In recent years, the field successfully exploited the performance of planning systems for PGR. The main benefits are the presence of efficient solvers and well-established, compact formalisms for behavior representation. However, the expressivity of the STRIPS planning models used so far is limited, and models in PGR are often structured in a hierarchical way. We present the approach Plan and Goal Recognition as HTN Planning that combines the expressive but still compact grammar-like HTN representation with the advantage of using unmodified, off-the-shelf planning systems for PGR. Our evaluation shows that - using our approach - current planning systems are able to handle large models with thousands of possible goals, that the approach results in high recognition rates, and that it works even when the environment is partially observable, i.e., if the observer might miss observations.

Novice users require assistance when performing handicraft tasks. Adequate instruction ensures task completion and conveys knowledge and abilities required to perform the task. We present an assistant teaching novice users how to operate electronic tools, such as drills, saws, and sanders, in the context of Do-It-Yourself (DIY) home improvement projects. First, the actions that need to be performed for the project are determined by a planner. Second, a dialogue manager capable of natural language interaction presents these actions as instructions to the user. Third, questions on these actions and involved objects are answered by generating appropriate ontology-based explanations.

Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachable parts of the task hierarchy. We show (both in theory and empirically) that rebuilding it during planning can improve heuristic accuracy thereby decreasing the explored search space. The evaluation further studies the heuristic both in terms of plan quality and coverage.

There are several formalizations for hierarchical planning. Many of them allow to specify preconditions and effects for compound tasks. They can be used, e.g., to assist during the modeling process by ensuring that the decomposition methods' plans “implement” the compound tasks' intended meaning. This is done based on so-called legality criteria that relate these preconditions and effects to the method's plans and pose further restrictions. Despite the variety of expressive hierarchical planning formalisms, most theoretical investigations are only known for standard HTN planning, where compound tasks are just names, i.e., no preconditions or effects can be specified. Thus, up to know, a direct comparison to other hierarchical planning formalisms is hardly possible and fundamental theoretical properties are yet unknown. To enable a better comparison between such formalisms (in particular with respect to their computational expressivity), we first provide a survey on the different legality criteria known from the literature. Then, we investigate the theoretical impact of these criteria for two fundamental problems to planning: plan verification and plan existence. We prove that the plan verification problem is at most NP-complete, while the plan existence problem is in the general case both semi-decidable and undecidable, independent of the demanded criteria. Finally, we discuss our theoretical findings and practical implications.

Modern technical devices are often too complex for many users to be able to use them to their full extent. Based on planning technology, we are able to provide advanced user assistance for operating technical devices. We present a system that assists a human user in setting up a complex home theater consisting of several HiFi devices. For a human user, the task is rather challenging due to a large number of different ports of the devices and the variety of available cables. The system supports the user by giving detailed instructions how to assemble the theater. Its performance is based on advanced user-centered planning capabilities including the generation, repair, and explanation of plans.

Technical systems evolve from simple dedicated task solvers to cooperative and competent assistants, helping the user with increasingly complex and demanding tasks.For this, they may proactively take over some of the users responsibilities and help to find or reach a solution for the user's task at hand, using e.g., Artificial Intelligence (AI) Planning techniques.However, this intertwining of user-centered dialog and AI planning systems, often called mixed-initiative planning (MIP), does not only facilitate more intelligent and competent systems, but does also raise new questions related to the alignment of AI and human problem solving.In this paper, we describe our approach on integrating AI Planning techniques into a dialog system, explain reasons and effects of arising problems, and provide at the same time our solutions resulting in a coherent, userfriendly and efficient mixed-initiative system.Finally, we evaluate our MIP system and provide remarks on the use of explanations in MIP-related phenomena.

Theoretical results on HTN planning are mostly related to the plan existence problem. In this paper, we study the structure of the generated plans in terms of the language they produce. We show that such languages are always context-sensitive. Furthermore we identify certain subclasses of HTN planning problems which generate either regular or context-free languages. Most importantly we have discovered that HTN planning problems, where preconditions and effects are omitted, constitute a new class of languages that lies strictly between the context-free and context-sensitive languages.