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From 12.06.05 to 17.06.2005 the Dagstuhl Seminar 05241 ``Synthesis and Planning'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

Most organizations have traditionally been organized by function, and most coordination is intrafunctional rather than interfunctional. However, many organizations are finding that they must also manage processes - such as order fulfillment, new product development, and interorganizational supply chain management - that span their separate functional units and that integrate their activities with those of other organizations. These processes are essential to the well-being of organizations in a dynamic competitive environment.In response to this, organizations are deploying large-scale enterprise information systems in order to support operational, tactical, and strategic decision making, along with information management. However, deployment of such information systems has not realized the requisite benefits due to issues such as lack of interoperability among applications due to technological evolution, constant changes to the business processes, evolving organizational structures, inherent complexity in management of distributed knowledge and resources.To ameliorate such issues, a recent technological trend is the adoption of support tools such as Workflow Management Systems (WFMS) and groupware to support coordination between individual and group knowledge worker activities respectively. While WFMSs mostly deal with tasks involving very structured information, groupware tools deal with tasks involving unstructured information. Due to these differences, such tools are used in a fragmented manner, causing information loss. The overall guiding design principles that can be used by such process support systems are minimal, resulting in costly overheads for organizations.This dissertation deals with the problems highlighted above from a organizational process design standpoint. The goal of the dissertation is to provide process designers with guidelines and tools that can assist them in modeling flexible and adaptable processes. The following two research questions a

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

We describe a system for specifying the effects of actions. Unlike those commonly used in Al planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of propositional theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed.

Planning is a central activity in many areas including robotics, manufacturing, space mission sequencing, and logistics. as the size and complexity of planning problems grow, there is great economic pressure to automate this process in order to reduce the cost of planning effort, and to improve the quality of produced plans.AI planning research has focused on general-purpose planning systems which can process the specifications of an application domain and generate solutions to planning problems in that domain. Unfortunately, there is a big gap between theoretical and application oriented work in AI planning. The theoretical work has been mostly based on state-based planning, which has limited practical applications. The application- oriented work has been based on hierarchical task network (HTN) planning, which lacks a theoretical foundation. As a result, in spite of many years of research, building planning applications remains a formidable task.The goal of this dissertation is to facilitate building reliable and effective planning applications. The methodology includes design of a mathematical framework for HTN planning, analysis of this framework, development of provably correct algorithms based on this analysis, and the implementation of these algorithms for further evaluation and exploration. The representation, analyses, and algorithms described in this thesis will make it easier to apply HTN planning techniques effectively and correctly to planning applications. The precise and mathematical nature of the descriptions will also help teaching about HTN planning, will clarify misconceptions in the literature, and will stimulate further research.

Although AI planning techniques can potentially be useful in several manufacturing domains, this potential remains largely unrealized. In order to adapt AI planning techniques to manufacturing, it is important to develop more realistic and robust ways to address issues important to manufacturing engineers. Furthermore, by investigating such issues, AI researchers may be able to discover principles that are relevant for AI planning in general. As an example, in this paper we describe the techniques for manufacturing-operation planning used in IMACS (Interactive Manufacturability Analysis and Critiquing System), and compare and contrast them with the techniques used in classical AI planning systems. We describe how one of IMACS's planning techniques may be useful for AI planning in general -- and as an example, we describe how it helps to explain a puzzling complexity result in AI planning.

Roboter können komplexe Aufgaben in stark kontrollierten industriellen Umgebungen bewältigen. Im Gegensatz dazu sind Alltagsumgebungen, wie z.B. ein Haushalt, für Roboter anspruchsvoller, da sie sowohl schnell auf Veränderungen reagieren als auch unter Echtzeitbedingungen arbeiten müssen. Darüber hinaus können diese realen Umgebungen unsicher sein, vor allem, weil sie den Menschen einbeziehen. Informationen über die Welt sind in der Regel unvollständig, was viele Schwierigkeiten mit sich bringt. In einigen Fällen müssen Roboter verschiedene unbekannte Objekte ohne Vorkenntnisse manipulieren. Diese Dissertation schlägt vor, mit Vorhersagemodellen den Echtzeit-Betrieb und die Manipulation von Objekten durch einen Roboter im Falle von unvollständigem Wissen über eine Umgebung zu verbessern. Speziell befasst sich diese Dissertation mit den Problemen der nichtgreifenden Manipulation von unbekannten Objekten und der Aktionsplanung in unordendlichen Umgebungen. Pushing ist eine häufige, nichtgreifende Manipulation in Roboterszenarien. Im ersten Teil der Arbeit liegt der Schwerpunkt also auf dem Problem, unbekannte Objekte mit einer mobilen Roboterbasis zu verschieben. Diese Arbeit schlägt einen datengesteuerten Ansatz für das Online-Lernen von lokalen inversen Modellen der Roboter-Objekt-Push-Interaktion vor. Die Validierung zeigt ein hohes Maß an Robustheit und eine hohe Erfolgsrate für eine Vielzahl von Objekten. Darüber hinaus ist der Roboter in der Lage, Objekteigenschaften zu erlernen und zwischen verschiedenen Objektverhalten, basierend auf erlernten inversen Modellen, beim Schieben von verschiedenen Seiten zu unterscheiden. Eine Strategie für die Objektplazierung in komplexen dynamischen Umgebungen wird ebenfalls vorgestellt. Zu diesem Zweck wird das Konzept eines Schubkorridors vorgestellt, der es ermöglicht, Bedingungen zu definieren, die eine Hindernisvermeidung beim Schieben in unordendlichen Umgebungen gewährleisten. Die Ergebnisse zeigen, dass ein Roboter in d, Robots can handle complex tasks in highly controlled industrial settings. In contrast, everyday environments, for example a household environment, are more challenging for robots as they have to react quickly to changes as well as operate under real-time constraints. Moreover, these real-world environments can be uncertain, primarily because they include humans. Information about the world is usually incomplete, which poses many difficulties in robot operation. In some cases, robots need to manipulate various unknown objects without prior experience. The work done in this thesis proposes using predictive models that enable real-time operation and manipulation of objects by a robot in the case of incomplete knowledge about an environment. More specifically, the thesis addresses the problems of nonprehensile manipulation of unknown objects and action planning under uncertainty in robot environments. Pushing is a common nonprehensile manipulation in robotic scenarios. Thus, in the first part of the thesis, the focus is on the problem of pushing unknown objects with a mobile robot base. This work proposes a data-driven approach for online learning of local inverse models of robot-object pushing interaction. Validation demonstrates a high degree of robustness and a high success rate over a diverse set of objects. Moreover, a robot is able to learn object properties and distinguish between different object behaviours under pushing from different sides based on learned inverse models. A strategy for object delivery in complex dynamic environments is also presented. For this purpose, the concept of a pushing corridor which allows defining conditions to guarantee obstacle avoidance while pushing in scattered environments is introduced. Results demonstrate that a robot is able to successfully deliver various unknown objects in cluttered environments. The second part of the thesis deals with the planning of robot actions with incomplete and uncertain information. The standard, by Senka Krivić, Kumulative Dissertation aus sechs Artikeln, Dissertation University of Innsbruck 2019