3D Brawler Game Using a Hybrid Planning Approach

This paper extends hierarchical task network (HTN) planning with lightweight learning, considering that in robotics, actions have a non-zero probability of failing. Our work applies to A*-based HTN planners with lifting. We prove that the planner finds the plan of maximal expected utility, while retaining its lifting capability and efficient heuristic-based search. We show how to learn the probabilities online, which allows a robot to adapt by replanning on execution failures. The idea behind this work is to use the HTN domain to constrain the space of possibilities, and then to learn on the constrained space in a way requiring few training samples, rendering the method applicable to autonomous mobile robots.

Three approaches to hierarchical planning have been widely discussed in the recent planning literature: Hierarchical Task Network (HTN) decomposition, model-reduction and operator decomposition. Abstraction is used in different ways in these three approaches and this has significance for both efficiency and expressive power. This paper identifies four issues that arise in the use of abstraction in planning which have been treated in different ways in the three approaches identified above. These issues are discussed with reference to an approach to abstraction which combines elements of the HTN and operator-decomposition approaches. Particular comparison is made with the HTN approach in order to highlight some important distinctions between the task decomposition and operator decomposition planning strategies. The CNF (Common Normal Form) case study, used by Erol to demonstrate certain features of the HTN approach, is used as the basis for this comparison.

Hierarchical Task Networks (HTNs) are a common model for encoding knowledge about planning domains in the form of task decompositions. We present a novel algorithm that uses invariant analysis to construct an HTN from the PDDL description of a planning domain and a single representative instance. The algorithm defines two types of composite tasks that interact to achieve the goal of a planning instance. One type of task achieves fluents by traversing invariants in which only one fluent can be true at a time. The other type of task applies a single action, which first involves ensuring that the precondition of the action holds. The resulting HTN can be applied to any instance of the planning domain, and is provably sound. We show that the performance of our algorithm is comparable to algorithms that learn HTNs from examples and use added knowledge.

Currently, purely deep learning-based agents struggle to make optimal decisions within a short timeframe in problems with a vast decision-making space. Human planning knowledge is required to assist agents in making better decisions. This manuscript proposes a novel knowledge-guided and data-driven decision-making framework, utilizing hierarchical task network as the carrier of knowledge, deep learning as the trainer for data, and the Monte Carlo Tree Search as the connector between hierarchical task network and deep learning. The experiments on the MiniRTS environment validated that the proposed framework in this manuscript can replace humans in collecting high-quality data, and it can train neural networks that perform equally well as the compared network even with only 20% of the available data, which provide a new direction for future research.

Web Question Answering (WQA) and Web Service (WS) are parallel fields in intelligent web computing. In network services, they are used widely, and they are rarely combined together. For many intelligent web applications, however, using them together tends to be important. This paper based on the study of significant techniques of WQA, WS and Hierarchical Task Network (HTN), including Natural Language Processing (NLP), Web Service Ontology and a planner of HTN, gives a proposal of WQA framework. This framework combines NLP, predicate logic, Semantic Web description and an HTN planner-SHOP2, turning question answering into a planning problem by question mapping and service planning. It addresses a transition from natural language to semantic expression, composites web services dynamically by using SHOP2, and obtains the answering that meets customers’ question by executing the web services sequence.

When an actor executes a plan, action failures and exogenous events may lead to unexpected states that require replanning from the middle of plan execution. In Hierarchical Task Network (HTN) planning, unless the HTN methods have been carefully written to work well in unexpected states, replanning may either fail or produce plans that perform poorly.
 To overcome this problem, we introduce IPyHOP, a reentrant version of GTPyhop (a SHOP-like HTN planner), and Run-Lazy-Refineahead, a modification of the Run-Lazy-Lookahead actor that utilizes IPyHOP's reentrant replanning capability to replan during plan execution. In our experiments, Run-Lazy-Refineahead and IPyHOP expend less search effort (fewer decompositions and fewer iterations), find revised plans with fewer actions and lower total action cost, and finish execution with fewer failures.

During task execution by a space manipulator, many factors need to be considered, such as joint motion range, external environment interference, running speed and task time. Studying the motion planning strategy under multi-constraint conditions is important for optimizing the task flow of the manipulator and improving the planning autonomy. Aiming at this, this paper proposes a method based on a hierarchical task network which has the ability to connect non-adjacent nodes of topology when generating the best path; it also carries out the control simulation of traditional path planning of a manipulator for typical tasks and the latter two working conditions using an optimal task planning method. The results show that this method is effective for realizing the Cartesian spatial task planning of a manipulator under multiple constraints and has important on-orbit application significance for improving the continuous autonomy of task completion.

In the context of a business process modeled by commitments, agents enact a protocol by carrying out goals that service their part of commitments. In a competitive or even in a cooperative setting, an agent does not know for sure that its partners will successfully act on their part of the commitments. We introduce uncertainty into a successful recent approach of planning first-order commitment protocols. Probabilities reflect a semantics of the belief of an agent about the successful completion of tasks by other agents within the protocol, capturing notions of trust. We take a deterministic Hierarchical Task Network (HTN) planner, introduce probabilities into the task networks, and derive a protocol enactment which maximizes expected utility from the point of view of one agent. We illustrate our approach on a business scenario in e-commerce.

Researches of AI planning in Real-Time Strategy (RTS) games have been widely applied to human behavior modeling and war simulation. Due to the fog-of-war, planning in RTS games need to be implemented under partially observable environment, which poses a big challenge for researchers. This paper focuses on extending Hierarchical Task Network (HTN) Planning in partially observable environment, and proposes a partially observable adversarial hierarchical task network planning with repairing algorithm named PO-AHTNR. By adding sensing action into HTN domain knowledge, a reconnaissance strategy and a history-based single belief state generation method are presented to obtain the best action. In order to verify the proposed algorithm, an empirical study based on µRTS game is carried out, and the performance of modified algorithm is compared to that of AHTNR and other state-of-the-art search algorithms developed for RTS games.

Action planning has been used in the field of robotics for solving long-running tasks. However, the definition of a planning problem for a complex, real-world robot is not trivial. The planning process could become intractable as its search spaces become large. The dissertation presented in this article introduces a novel approach which amalgamates Description Logic (dl) reasoning with Hierarchical Task Network (htn) planning. The planning domain description as well as fundamental htn planning concepts are represented in dl and can therefore be subject to dl reasoning; from these representations, concise planning problems are generated for htn planning.

This paper proposes a plan creation and execution system used by the agent HTN Fighter in the Fighting ICE game framework. The underlying approach implements a Hierarchical Task Network (HTN) planner and a simple planning domain that focuses on sequences of close-range attacks. The execution process is tightly interleaved with the planning process compensating for the uncertainty caused by the delay of 15 frames which the information about the game world state is provided with. Using an HTN and the proposed execution system, the agent is able to follow high-level strategies staying reactive to changes in the environment. Experiments show that HTN Fighter outperforms the sample MCTS controller and the top three controllers submitted to the 2016 Fighting Game AI Competition.

This paper presents the underlying technology of OncoTheraper, a Clinical Decision Support System for oncology therapy planning. The paper is focused on the representation of oncology treatment protocols by a temporally extended, Hierarchical Task Networks (HTN) based knowledge representation as well as their interpretation by a temporal HTN planning process. The planning process allows to obtain temporally annotated therapy plans that support decisions of oncologists in the area of paediatrics oncology. A proof on concept aimed to validate this technology is also described. In addition, a service oriented architecture that supports the decision services of the system is proposed.

Hierarchical task network planning with resources and temporal constraints

This article presents a concept for scalable autonomy of UAVs applicable to the field of reconnaissance missions. Level of autonomy can be defined along the amount of human independence. Low human independence allows detailed control, while adding to the workload. Higher human independence provides functionalities inflicting less workload, but hides information from the pilot or inhibits from interacting with the system in depth. We present a concept to facilitate a range of human independence provided by an intelligent agent onboard the UAV. This enables the interaction of the human with the automation on different echelons of control. Instead of offering access to the different automation functions directly, the operator is guided along a hierarchical task tree. This allows the system to deduce the overall purpose of human interactions and facilitates support by informing about flaws in the plan and by suggesting alternatives. The concept was implemented resulting in an agent architecture, which is based on Hierarchical Task Network (HTN) planning and reasoning with the use of the Drools rule engine. The agent is commanded using the concept of task-based guidance, which represents the highest human independence currently implemented in the system. The research for guiding multiple UAVs on a team level is planned as a next step. We conducted tests with the implemented system in a simulated environment.

To scale-up to real-world problems, planning systems must be able to replan in order to deal with changes in problem context. In this paper we describe hierarchical task network and operator-based replanning techniques which allow adaptation of a previous plan to account for problems associated with executing plans in real-world domains with uncertainty, concurrency, changing objectives. We focus on replanning which preserves elements of the original plan in order to use more reliable domain knowledge and to facilitate user understanding of produced plans. We present empirical results documenting the effectiveness of these techniques in a NASA antenna operations application.