Knowledge-Based Task Structure Planning for an Information Gathering Agent

An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed

Search Complexities for HTN Planning

Hierarchical Task Network (HTN) planning is the problem of decomposing an initial task into a sequence of executable steps. Often viewed as just a way to encode human knowledge to solve classical planning problems faster, HTN planning is more expressive than classical planning, even to the point of being undecidable in the general case. However, HTN planning is not just a way to solve planning problems faster, but is itself a search problem that can benefit from its own distinct search algorithms and heuristics. The dissertation examines the complexities of various HTN planning problem classes in order to motivate the development of heuristic search algorithms for HTN planning which are guaranteed to terminate on a large class of syntactically identifiable problems, as well as domain independent heuristics for those algorithms to use. This will allow HTN planning to be used in a number of areas where the solvability of a problem is in question, including during the initial development of a domain and for use in policy generation in non-deterministic planning environments. In particular, this dissertation analyzes two commonly used algorithms for HTN planning and describes the subsets of HTN problems that these algorithms terminate on. This allows us to discuss the run-times of these algorithms and com- pare the expressivity of the classes of problems they decide. We provide two new HTN algorithms which terminate on a strictly broader and more expressive set of HTN problems. We also analyze the complexity of delete-free HTN planning, an analogue to delete-free classical planning which is the base of many classical planning heuristics. We show that delete-free HTN planning is NP-complete, putting the existence of strict-semantics delete-relaxation-based HTN heuristics out of reach for practical purposes. Finally, we provide a translation of a large subset of HTN planning to classical planning, which allows us to use a classical planner as a surrogate for a heuristic HTN planner. Our experiments show that even small amounts and incomplete amounts of HTN knowledge, when translated into PDDL using our algorithm, can greatly improve a classical planner's performance

Knowledge engineering techniques for automated planning

Formulating knowledge for use in AI Planning engines is currently some-thing of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. Also, there is little published research to inform engineers on which method and tools to use in order to effectively engineer a new planning domain model. This is of growing importance, as domain independent planning engines are now being used in a wide range of applications, with the consequence that op-erational problem encodings and domain models have to be developed in a standard language. In particular, at the difficult stage of domain knowledge formulation, changing a statement of the requirements into something for-mal - a PDDL domain model - is still somewhat of an ad hoc process, usually conducted by a team of AI experts using text editors. On the other hand, the use of tools such as itSIMPLE or GIPO, with which experts gen-erate a high level diagrammatic description and automatically generate the domain model, have not yet been proven to be more effective than hand coding. The major contribution of this thesis is the evaluation of knowledge en-gineering tools and techniques involved in the formulation of knowledge. To support this, we introduce and encode a new planning domain called Road Traffic Accidents (RTA), and discuss a set of requirements that we have derived, in consultation with stakeholders and analysis of accident management manuals, for the planning part of the management task. We then use and evaluate three separate strategies for knowledge formulation, encoding domain models from a textual, structural description of require-ments using (i) the traditional method of a PDDL expert and text editor (ii) a leading planning GUI with built in UML modelling tools (iii) an object-based notation inspired by formal methods. We evaluate these three ap-proaches using process and product metrics. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning. In addition, we discuss a range of state-of-the-art modelling tools to find the types of features that the knowledge engineering tools possess. These features have also been used for evaluating the methods used. We bench-mark our evaluation approach by comparing it with the method used in the previous International Competition for Knowledge Engineering for Plan-ning & Scheduling (ICKEPS). We conclude by providing a set of guide-lines for building future knowledge engineering tools

Egocentric Planning for Scalable Embodied Task Achievement

Embodied agents face significant challenges when tasked with performing actions in diverse environments, particularly in generalizing across object types and executing suitable actions to accomplish tasks. Furthermore, agents should exhibit robustness, minimizing the execution of illegal actions. In this work, we present Egocentric Planning, an innovative approach that combines symbolic planning and Object-oriented POMDPs to solve tasks in complex environments, harnessing existing models for visual perception and natural language processing. We evaluated our approach in ALFRED, a simulated environment designed for domestic tasks, and demonstrated its high scalability, achieving an impressive 36.07% unseen success rate in the ALFRED benchmark and winning the ALFRED challenge at CVPR Embodied AI workshop. Our method requires reliable perception and the specification or learning of a symbolic description of the preconditions and effects of the agent's actions, as well as what object types reveal information about others. It is capable of naturally scaling to solve new tasks beyond ALFRED, as long as they can be solved using the available skills. This work offers a solid baseline for studying end-to-end and hybrid methods that aim to generalize to new tasks, including recent approaches relying on LLMs, but often struggle to scale to long sequences of actions or produce robust plans for novel tasks

Coordinating services embedded everywhere via hierarchical planning

The spaces we live in are provided with different devices and technologies, such as sensors for recognising our presence. The aim of such spaces is to improve our comfort, productivity, and even reduce our energy bills. The problem with fulfilling the aim is that devices alone cannot do much to achieve such difficult goals. People would also have problems in manually searching for the best situation accomplishing their needs. A way to deal with this problem is to coordinate devices automatically. For example, our home can autonomously figure out that some lamps can be turned off because the living room has enough natural light and the activity we are currently doing requires a low light level. The benefits are improved comfort and a reasonable amount of energy saved. We therefore explore the possibilities of using a system based on automated planning. This planning produces a set of device services, such as turn off a lamp, that achieves a given goal. We use a method, called hierarchical planning, which enables us to organise the knowledge we have about spaces and devices in hierarchical forms. We show that planning is suitable for this kind of problems by using hierarchical planning to save energy in the Bernoulliborg building at the University of Groningen. The results show energy and money savings, and that people are satisfied with our system. We also improve the system and show that even more money can be saved without sacrificing the well-being of people if we can buy energy from several energy providers

Mission programming for flying ensembles: combining planning with self-organization

The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness. In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.Der Einsatz von autonomen mobilen Robotern kann viele Abläufe unseres täglichen Lebens erleichtern. Ihr Einsatz kann Arbeitsbedingungen verbessern, als innovative Technik für verschiedene Forschungsdisziplinen dienen oder Rettungskräfte im Einsatz unterstützen. Insbesondere Flugroboter haben ihr Potenzial bereits in vielerlei Anwendungsfällen gezeigt, gerade wenn mehrere in Ensembles eingesetzt werden. Das Potenzial fliegender Ensembles zielgerichtet und anwendungsspezifisch auszuschöpfen erfordert ausgefeilte Programmiermethoden und Koordinierungsverfahren. Zu diesem Zweck sind zuletzt viele unterschiedliche und auf speziell entwickelte Roboter zugeschnittene Softwareansätze entstanden. Diese verwenden oft klassische Planungs-, Scheduling-, Optimierungs- und Reasoningverfahren. Während dies vor allem den zielgerichteten Einsatz von Ensembles ermöglicht, ist es jedoch auch oft ineffizient, wenn die Ensembles größer oder deren Einsatzumgebungen unsicher werden. Die genannten Nachteile können durch das Paradigma der Selbstorganisation kompensiert werden: Falls Anwendungen nicht zwangsläufig auf Optimalität und strikte Zielorientierung ausgelegt sind, kann so Skalierbarkeit und Robustheit im System erreicht werden. In dieser Arbeit werden die vorteilhaften Eigenschaften klassischer Planungstechniken mit denen der Selbstorganisation in einem Ansatz zur Missionsprogrammierung für fliegende Ensembles kombiniert. In der dafür entwickelten Lösung wird von der aktuell etablierten Ansicht einer unveränderlichen Roboterkonstruktion abgewichen. Stattdessen wird die Hardwarezusammenstellung der Roboter als zur Laufzeit modifizierbar angesehen. Der Einsatz solcher Roboter erfordert neue Softwareansätze um mit genannter Flexibilität umgehen zu können. Die hier vorgestellten Beiträge zu diesem Thema lassen sich in drei Punkten zusammenfassen: Erstens wird eine Schichtenarchitektur als Referenz für physikalisch konfigurierbare Roboterensembles vorgestellt. Zweitens wird eine Lösung zur zielorientierten Missions-Programmierung für derartige Ensembles präsentiert, mit der sowohl einzelne Roboter als auch ganze Ensembles instruiert werden können. Drittens werden mehrere Selbstorganisationsmechanismen vorgestellt, die die autonome Ausführung so erstellter Missionen ermöglichen. Durch die Kombination verschiedener Selbstorganisationsmechanismen wird sichergestellt, dass Ensembles die missionsspezifischen Anforderungen erfüllen. Zusätzliche Selbstorganisationsmechanismen ermöglichen die koordinierte Ausführung der Missionen durch die Ensembles. Darüber hinaus bietet diese Lösung die Möglichkeit der Integration zielorientierten Schwarmverhaltens. Durch ein allgemeines algorithmisches Verfahren für auf Trajektorien-Modifikation basierendes Schwarmverhalten können allein durch die Änderung des Parametersatzes unterschiedliche emergente Effekte in einer Mission erzielt, quantifiziert und weiterverarbeitet werden. Zur theoretischen und praktischen Evaluierung der Ergebnisse dieser Arbeit wurden die vorgestellten Techniken in verschiedenen Fallstudien auf simulierter sowie realer Hardware zum Einsatz gebracht