Hierarchical Task Network Research Articles

On Succinct Groundings of HTN Planning Problems

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.

Improving AI Planning using Map Reduce

Today, the Landmark concept is adapted from the classical planning to work in hierarchical task network planning. It was shown how it is used to extracts landmark literals from a given hierarchical planning domain and problem description and then use these literals to update the the planning domain by ruling out the irrelevant tasks and methods before the actual planning is performed. In this paper, we compine the landmark concept with the Map-reduce framework to increase the performance of the planning process. Our empirical evaluation shows that the combination between landmark and Map-Reduce framework dramatically improves performance of the planning process.

Composition modeling for manufacturing resource cloud service

The lack of semantic information of description model and quality of service (QoS) constraints in manufacturing resource cloud service (MRCS) has caused problems such as uneven distribution, small sharing scope and low standardization of manufacturing resources. The paper studies composition modeling of MRCS for this problem. Firstly, a modeling method for MRCS based on semantic extension is proposed. The formalization method is used to classify manufacturing resources, and the 6-layer semantic information of function, interface, execution, state, QoS and physical-virtual resource mapping is attached to the MRCS, and a data model based on eXtensible Markup Language and a semantic model based on ontology are constructed. Then a composition method for MRCS with multi-constraint of QoS is proposed. A composition framework and process for cloud services with multi-constraint of QoS is planned. A planning method based on hierarchical task network (HTN) is used to decompose manufacturing tasks into atomic tasks. The manufacturing task is decomposed into atomic tasks and used as service requirements by a planning method based on HTN, and multiple independent and related cloud services are composed into a new cloud service according to application needs through interface semantic matching. The attribute weight of the scheme for MRCS is comprehensively calculated based on quality function deployment. The composite cloud service corresponding to each atomic task is used as an independent component service, and the interface semantic matching operation is repeated according to the workflow of the atomic task to form a complete composite cloud service with more powerful functions to realize the manufacturing task.

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

For task planning of the command and control structure, the existing algorithms exhibit low efficiency and poor replanning quality under abnormal conditions. Given the requirements of the current accusation architecture, a distributed command and control structure model is built in this paper based on multiagents, which exploits the superiority of multiagents in achieving complex tasks. The concept of MultiAgent-HTN is proposed based on the framework. The original hierarchical task network planning algorithm is optimized, the multiagent collaboration framework is redefined, and the coordination mechanism of local conflict is developed. With the classical resource scheduling problem as the experimental background, the proposed algorithm compared with the classical HTN algorithm is drawn. According to the experimental results, the proposed algorithm exhibits higher quality and higher efficiency than the existing algorithm and the space anomaly is significant in the course of processing. The planning is more efficient and the time is more complicated and superior in solving the same problem, and the algorithm exhibits good convergence and adaptability. In the conclusion, it is proved that the distributed command and control structure proposed in this paper exhibits high practicability in relevant fields and can solve the problem of distributed command and control structure in a multiagent scenario.

Bringing Order to Chaos – A Compact Representation of Partial Order in SAT-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.

Hybrid planning and distributed iterative repair for multi-robot missions with communication losses

This paper presents a planning and execution architecture suited for the initial planning, the execution and the on-board repair of a plan for a multi-robot mission. The team as a whole must accomplish its mission while dealing with online events such as robots breaking down, new objectives for the team, late actions and intermittent communications. We have chosen a “plan then repair” approach where an initial plan is computed offline and updated online whenever disruptive events happen. We have defined an hybrid planner that mixes Partial Order Planning (POP) with a Hierarchical Task Network (HTN)-based modelling of actions. This planner, called HiPOP for Hierarchical Partial-Order Planner, computes plans with temporal flexibility (thus easing its execution) and abstract actions (thus easing the repair process). It uses a symbolic representation of the world and has been extended with geometrical reasoning to adapt to multi-robots missions. Plans are executed in a distributed way: each robot is responsible of executing its own actions, and to propagate delays in its local plan, taking benefit from the temporal flexibility of the plan. When an inconsistency or a failure arises, a distributed repair algorithm based on HiPOP is used to repair the plan, by iteratively removing actions in the plan in order to amend the global plan. This repair is done onboard one of the robot of the team, and takes care of partial communication. The whole architecture has been evaluated through several benchmarks, statistical simulations, and field experiments involving 8 robots.

Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations

Internet of Things simulations play significant roles in the diverse kinds of activities in our daily lives and have been extensively researched. Creating and controlling virtual agents in three-dimensional Internet of Things simulations is a key technology for achieving realism in three-dimensional simulations. Given that traditional virtual agent-based approaches have limitations for realism, it is necessary to improve the realism of three-dimensional Internet of Things simulations. This article proposes a Q-Network-based motivation framework that applies a Q-Network to select motivations from desires and hierarchical task network planning to execute actions based on goals of the selected motivations. The desires are to be identified and calculated based on states. Selected motivations will be chosen to determine the goals that agents must achieve. In the experiments, the proposed framework achieved an average accuracy of up to 85.5% when the Q-Network-based motivation model was trained. To verify the Q-Network-based motivation framework, a traditional Q-learning is also applied in the three-dimensional virtual environment. Comparing the results of the two frameworks, the Q-Network-based motivation framework shows better results than those of traditional Q-learning, as the accuracy of the Q-Network-based motivation is higher by 15.58%. The proposed framework can be applied to the diverse kinds of Internet of Things systems such as a training autonomous vehicle. Moreover, the proposed framework can generate big data on animal behaviors for other training systems.

HIERARCHICAL TASK NETWORK APPROACH FOR TIME AND BUDGET CONSTRAINED CONSTRUCTION PROJECT PLANNING

Completing a construction project on time and within budget is of great importance in the construction industry. To achieve this goal, a construction plan satisfying the time and cost constraints is crucial. While a rich amount of literature on the time-cost trade-off scheduling and time/cost optimization scheduling has been presented, developing a construction plan for the time and cost-constrained construction project has not been fully explored. This study presented a hierarchical task network (HTN) based construction planning model to fill this gap. First of all, a knowledge formalism catering to the HTN planning was provided to accommodate the construction planning knowledge. Then, the planning process was explained in detail, including temporal reasoning used to sequence the construction activities, and backtracking evasion mechanism used to avoid the trouble of backtracking due to inappropriate selection of execution modes for construction activities. Finally, two sets of comparisons based on a fictional construction project were performed, the results of which demonstrate that the time and budget constraints have an impact on the section of execution modes for construction methods, and the proposed planning model can develop construction plan that satisfies the specified deadline and budget limitations effectively regardless of the existing of backtracking.

Intelligent Task Resource Planning Based on Adaptive Dynamic Resource Composition Model

With the rapid development of information technology, various resources, including computing, network, storage and application, generally present the trend of servitization. In order to plan these resources dynamically in a short time according to the task requirement and allocate them properly, this paper propose an adaptive dynamic resource composition model which can realize fast searching and matching of resources through automatic classification of resources based on type, Hierarchical Task Network (HTN) planning to implement the task planning and resource allocation algorithm to implement the resource allocation. We design a task resource intelligent planning system with a specific scenario to verify our theory and method.

Fuzzy Theory Based Single Belief State Generation for Partially Observable Real-Time Strategy Games

As the basic problem of the real-time strategy (RTS) games, AI planning has attracted wide attention of researchers, but it still remains as a huge challenge due to its large searching space and real-time nature. The situation may get worse when the planning in RTS games is implemented under a partially observable environment considering the existence of the fog-of-war. Given the recorded past positions of an agent, it would be helpful if the targets' next position can be predicted based on the recorded data since this will increase the certainty of the target. Therefore, this paper proposes a fuzzy theory-based single belief state generation method named FTH to do what based on multi-layer information sets extracted from the history position information. Besides, we incorporate the FTH generation method into adversarial hierarchical task network repairing (AHTNR) planning algorithm, which can be used for the prediction of the unit's position and task planning. Finally, we carry out an empirical study based on the μRTS game and validate its effectiveness by comparing its performance with that of other state-of-the-art algorithms.

Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning.

The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized conciliation of multiple guidelines considering additionally patient preferences brings some further difficulties. Recently, several works have explored distinct techniques to come up with an automated process for the conciliation of clinical guidelines for comorbid patients but very little attention has been put in integrating the patient preferences into this process. In this work, a Multi-Agent Planning (MAP) framework that extends previous work on single-disease temporal Hierarchical Task Networks (HTN) is proposed for the automated conciliation of clinical guidelines with patient-centered preferences. Each agent encapsulates a single-disease Computer Interpretable Guideline (CIG) formalized as an HTN domain and conciliates the decision procedures that encode the clinical recommendations of its CIG with the decision procedures of the other agents' CIGs. During conciliation, drug-related interactions, scheduling constraints as well as redundant actions and multiple support interactions are solved by an automated planning process. Moreover, the simultaneous application of the patient preferences in multiple diseases may potentially bring about contradictory clinical decisions and more interactions. As a final step, the most adequate personalized treatment plan according to the patient preferences is selected by a Multi-Criteria Decision Making (MCDM) process. The MAP approach is tested on a case study that builds upon a simplified representation of two real clinical guidelines for Diabetes Mellitus and Arterial Hypertension.

Planning with actively eliciting preferences

Planning with preferences has been employed extensively to quickly generate high-quality plans. However, it may be difficult for the human expert to supply this information without knowledge of the reasoning employed by the planner. We consider the problem of actively eliciting preferences from a human expert during the planning process. Specifically, we study this problem in the context of the Hierarchical Task Network (HTN) planning framework as it allows easy interaction with the human. We propose an approach where the planner identifies when and where expert guidance will be most useful and seeks expert’s preferences accordingly to make better decisions. Our experimental results on several diverse planning domains show that the preferences gathered using the proposed approach improve the quality and speed of the planner, while reducing the burden on the human expert.

A system for generating storyline visualizations using hierarchical task network planning

Existing storyline visualization techniques present narratives as a node-link graph where a sequence of links shows the evolution of causal and temporal relationships between characters in the narrative. These techniques make a number of simplifying assumptions about the narrative structure, however. They assume that all narratives progress linearly in time, with a well-defined beginning, middle, and end. They assume that the narrative is complete prior to visualization. They also assume that at least two participants interact at every event. Finally, they assume that all events in the narrative occur along a single timeline. Thus, while existing techniques are suitable for visualizing linear narratives, they are not well suited for visualizing narratives with multiple timelines, non-linear narratives such as those with flashbacks, or for narratives that contain events with only one participant. In our previous work we presented Yarn, a system for automatic construction and visualization of narratives with multiple timelines. Yarn employs hierarchical task network planning to generate all possible narrative timelines and visualize them in a web-based interface. In this work, we extend Yarn to support non-linear narratives with flashbacks and flash-forwards, and non-linear point-of-view narratives. Our technique supports both single-participant as well as multi-participant events in the narrative, and constructs both linear as well as non-linear narratives. Additionally, it enables pairwise comparison within a group of multiple narrative timelines.

A Hybrid Approach to Planning and Execution in Dynamic Environments Through Hierarchical Task Networks and Behavior Trees

Intelligent autonomous agents that are acting in dynamic environmentsin real-time are often required to follow long-termstrategies while also remaining reactive and being able to actdeliberately. In order to create intelligent behaviors for videogame characters, there are two common approaches – plannersare used for long-term strategical planning, whereas BehaviorTrees allow for reactive acting. Although both methodologieshave their advantages, when used on their own, theyfail to fully achieve both requirements described above. Inthis work, we propose a hybrid approach combining a HierarchicalTask Network planner for high-level planning whiledelegating low-level decision making and acting to BehaviorTrees. Furthermore, we compare this approach with a pureplanner in a multi-agent environment.

A Generic Method to Guide HTN Progression Search with Classical Heuristics

HTN planning combines actions that cause state transition with grammar-like decomposition of compound tasks that additionally restricts the structure of solutions. There are mainly two strategies to solve such planning problems: decomposition-based search in a plan space and progression-based search in a state space. Existing progression-based systems do either not rely on heuristics (e.g. SHOP2) or calculate their heuristics based on extended or modified models (e.g. GoDeL). Current heuristic planners for standard HTN models (e.g. PANDA) use decomposition-based search. Such systems represent search nodes more compactly due to maintaining a partial order between tasks, but they have no current state at hand during search. This makes the design of heuristics difficult. In this paper we present a progression-based heuristic HTN planning system: We (1) provide an improved progression algorithm, prove its correctness, and empirically show its efficiency gain; and (2) present an approach that allows to use arbitrary classical (non-hierarchical) heuristics in HTN planning. Our empirical evaluation shows that the resulting system outperforms the state-of-the-art in HTN planning.

Validation of Hierarchical Plans via Parsing of Attribute Grammars

An important problem of automated planning is validating if a plan complies with the domain model. Such validation is straightforward for classical sequential planning but until recently there was no plan validation approach for Hierarchical Task Networks (HTN). In this paper we propose a novel technique for validating HTN plans by parsing of attribute grammars with the timeline constraint.

Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.

Simultaneous learning of hierarchy and primitives for complex robot tasks

We present a new interaction paradigm for robot learning from demonstration, called simultaneous learning of hierarchy and primitives (SLHAP), in which information about hierarchy and primitives is naturally interleaved in a single, coherent demonstration session. A key innovation in the new paradigm is the human demonstrator’s narration of primitives as he executes them, which allows the system to identify the boundaries between primitives. Hierarchy is represented using hierarchical task networks; motion planning constraints on the primitives are represented using task space regions. We implemented SLHAP on an autonomous robot and produced an interaction video illustrating its effectiveness learning a complex task with five levels of hierarchy and eight types of primitives. The underlying algorithms which make SLHAP possible are described and evaluated.

TotSAT - Totally-Ordered Hierarchical Planning Through SAT

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.

GoCo: planning expressive commitment protocols

This article addresses the challenge of planning coordinated activities for a set of autonomous agents, who coordinate according to social commitments among themselves. We develop a multi-agent plan in the form of a commitment protocol that allows the agents to coordinate in a flexible manner, retaining their autonomy in terms of the goals they adopt so long as their actions adhere to the commitments they have made. We consider an expressive first-order setting with probabilistic uncertainty over action outcomes. We contribute the first practical means to derive protocol enactments which maximise expected utility from the point of view of one agent. Our work makes two main contributions. First, we show how Hierarchical Task Network planning can be used to enact a previous semantics for commitment and goal alignment, and we extend that semantics in order to enact first-order commitment protocols. Second, supposing a cooperative setting, we introduce uncertainty in order to capture the reality that an agent does not know for certain that its partners will successfully act on their part of the commitment protocol. Altogether, we employ hierarchical planning techniques to check whether a commitment protocol can be enacted efficiently, and generate protocol enactments under a variety of conditions. The resulting protocol enactments can be optimised either for the expected reward or the probability of a successful execution of the protocol. We illustrate our approach on a real-world healthcare scenario.