HTN planning requires the decomposition of compound tasks into primitive and executable actions. In the currently most frequently used formalism, compound tasks lack explicit preconditions and effects. Those are, however, useful, e.g., for pruning techniques, heuristics, or the comprehension of domains. Previously, we introduced and formalized different kinds of inferred preconditions and effects of compound tasks based on their decomposition methods together with a complexity analysis. In this paper, we present an empirical evaluation of computing these inferred preconditions and effects using the IPC benchmark sets. Specifically, we analyze their frequency of occurrence and compare the performance of an approximation to the exact preconditions and effects. Our goal is to provide a comprehensive overview of the proposed techniques, enabling researchers to determine the extent to which they can be utilized in their given application.

In Hierarchical Task Network (HTN) planning, compound tasks need to be refined into executable (primitive) action sequences. In contrast to their primitive counterparts, compound tasks do not specify preconditions or effects. Thus, their implications on the states in which they are applied are not explicitly known: they are "hidden" in and depending on the decomposition structure. We formalize several kinds of preconditions and effects that can be inferred for compound tasks in totally ordered HTN domains. As relevant special case we introduce a problem relaxation which admits reasoning about preconditions and effects in polynomial time. We provide procedures for doing so, thereby extending previous work, which could only deal with acyclic models. We prove our procedures to be correct and complete for any totally ordered input domain. These results are embedded into an encompassing complexity analysis of the inference of preconditions and effects of compound tasks, an investigation that has not been made so far.

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.

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.

Recent trends in human–computer interaction (HCI) show a development towards cognitive technical systems (CTS) to provide natural and efficient operating principles. To do so, a CTS has to rely on data from multiple sensors which must be processed and combined by fusion algorithms. Furthermore, additional sources of knowledge have to be integrated, to put the observations made into the correct context. Research in this field often focuses on optimizing the performance of the individual algorithms, rather than reflecting the requirements of CTS. This paper presents the information fusion principles in CTS architectures we developed for Companion Technologies. Combination of information generally goes along with the level of abstractness, time granularity and robustness, such that large CTS architectures must perform fusion gradually on different levels — starting from sensor-based recognitions to highly abstract logical inferences. In our CTS application we sectioned information fusion approaches into three categories: perception-level fusion, knowledge-based fusion and application-level fusion. For each category, we introduce examples of characteristic algorithms. In addition, we provide a detailed protocol on the implementation performed in order to study the interplay of the developed algorithms.

Belief Propagation (BP) applied to cyclic problems is a well known approximate inference scheme for probabilistic graphical models. To improve the accuracy of BP, a divide-and-conquer approach termed Conditioned Belief Propagation (CBP) has been proposed in the literature. It recursively splits a problem by conditioning on variables, applies BP to subproblems, and merges the results to produce an answer to the original problem. In this essay, we propose a reformulated version of CBP that exhibits anytime behavior, and allows for more specific tuning by formalizing a further decision point that decides which subproblem is to be decomposed next. We propose some simple and easy to compute heuristics, and demonstrate their performance using an empirical evaluation on randomly generated problems.

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.

The properties of multimodality, individuality, adaptability, availability, cooperativeness and trustworthiness are at the focus of the investigation of Companion Systems. In this article, we describe the involved key components of such a system and the way they interact with each other. Along with the article comes a video, in which we demonstrate a fully functional prototypical implementation and explain the involved scientific contributions in a simplified manner. The realized technology considers the entire situation of the user and the environment in current and past states. The gained knowledge reflects the context of use and serves as basis for decision-making in the presented adaptive system.

Hybrid Planning combines Hierarchical Task Network (HTN) planning with concepts known from Partial-Order Causal-Link (POCL) planning. We introduce novel heuristics for Hybrid Planning that estimate the number of necessary modifications to turn a partial plan into a solution. These estimates are based on the task decomposition graph that contains all decompositions of the abstract tasks in the planning domain. Our empirical evaluation shows that the proposed heuristics can significantly improve planning performance.

We prove a new complexity result for Partial-Order Causal-Link (POCL) planning which shows the hardness of refining a search node (i.e., a partial plan) to a valid solution given a delete effect-free domain model. While the corresponding decision problem is known to be polynomial in state-based search (where search nodes are states), it turns out to be intractable in the POCL setting. Since both of the currently best-informed heuristics for POCL planning are based on delete relaxation, we hope that our result sheds some new light on the problem of designing heuristics for POCL planning. Based on this result, we developed a new variant of one of these heuristics which incorporates more information of the current partial plan. We evaluate our heuristic on several domains of the early International Planning Competitions and compare it with other POCL heuristics from the literature.

Only few cognitive architectures have been proposed that cover the complete range from recognizers working on the direct sensor input, to logical inference mechanisms of classical artificial intelligence (AI). Logical systems operate on abstract predicates, which are often related to an action-like state transition, especially when compared to the classes recognized by pattern recognition approaches. On the other hand, pattern recognition is often limited to static patterns, and temporal and multi-modal aspects of a class are often not regarded, e.g. by testing only on pre-segmented data. Recent trends in AI aim at developing applications and methods that are motivated by data-driven real world scenarios, while the field of pattern recognition attempts to push forward the boundary of pattern complexity. We propose a new generic architecture to close the gap between AI and pattern recognition approaches. In order to detect abstract complex patterns, we process sequential data in layers. On each layer, a set of elementary classes is recognized and the outcome of the classification is passed to the successive layer such that the time granularity increases. Layers can combine modalities, additional symbolic information or make use of reasoning algorithms. We evaluated our approach in an on-line scenario of activity recognition using three layers. The obtained results show that the combination of concepts from pattern recognition and high-level symbolic information leads to a prosperous and powerful symbiosis.

In hierarchical planning, landmarks are tasks that occur on any search path leading from the initial plan to a solution. In this work, we present novel domain-independent planning strategies based on such hierarchical landmarks. Our empirical evaluation on four benchmark domains shows that these landmark-aware strategies outperform established search strategies in many cases.

This work addresses the problem of track association in person tracking. We propose a probabilistic model, based on Markov Logic Networks, that aims at associating the individual tracks emerging from a person tracking algorithm to the correct persons. For this purpose the continuous estimates of the object positions acquired by the tracking algorithm are mapped into discrete spatial regions, which are based on a floor plan of the environment. Experiments show that the described model is able to exploit the additional information contained inside the provided floor plan, and deliver good results compared to a state of the art person tracking algorithm despite the lossy discretization step. We discuss the engineered model in detail and give an empirical evaluation using an indoor setting.

We examine the problem of filtering for dynamic probabilistic systems using Markov Logic Networks. We propose a method to approximately compute the marginal probabilities for the current state variables that is suitable for online inference. Contrary to existing algorithms, our approach does not work on the level of belief propagation, but can be used with every algorithm suitable for inference in Markov Logic Networks, such as MCSAT. We present an evaluation of its performance on two dynamic domains.