AUTOMATED PLANNING OF PROCESS MODELS: THE CONSTRUCTION OF SIMPLE MERGES

Business processes evolve dynamically with changing business demands. Because of these fast changes, traditional process improvement techniques have to be adapted and extended since they often require a high degree of manual work. To reduce this degree of manual work, the automated planning of process models is proposed. In this context, we present a novel approach for an automated construction of the control flow structure simple merge (XOR join). This accounts for a necessary step towards an auto-mated planning of entire process models. Here we build upon a planning domain, which gives us a general and formal basis to apply our approach independently from a specific process modeling lan-guage. To analyze the feasibility of our method, we mathematically evaluate the approach in terms of key properties like termination and completeness. Moreover, we implement the approach in a process planning software and apply it to several real-world processes

Safe reinforcement learning in uncertain contexts

When deploying machine learning algorithms in the real world, guaranteeing safety is an essential asset. Existing safe learning approaches typically consider continuous variables, i.e., regression tasks. However, in practice, robotic systems are also subject to discrete, external environmental changes, e.g., having to carry objects of certain weights or operating on frozen, wet, or dry surfaces. Such influences can be modeled as discrete context variables. In the existing literature, such contexts are, if considered, mostly assumed to be known. In this work, we drop this assumption and show how we can perform safe learning when we cannot directly measure the context variables. To achieve this, we derive frequentist guarantees for multi-class classification, allowing us to estimate the current context from measurements. Further, we propose an approach for identifying contexts through experiments. We discuss under which conditions we can retain theoretical guarantees and demonstrate the applicability of our algorithm on a Furuta pendulum with camera measurements of different weights that serve as contexts.Comment: Accepted final version to appear in the IEEE Transactions on Robotic

Archäologischer Survey in Markgrafneusiedl/NÖ

Official report about archaeological investigations regarding the presence and absence of archaeological finds and structures through intensive systematic archaeological survey

Quantitative Approaches to enable the Automated Planning of Adaptive Process Models

Nowadays, process models are valuable tools for a variety of activities in the business environment. They are used, for example, to train employees, to document processes or as part of company audits and to align the IT strategy with the company goals. However, process models are still created manually in many cases. This manual creation proves to be tedious, thus cost-intensive and especially error-prone. The dissertation at hand addresses this problem area and presents approaches for the automated planning of adaptive process models. Adaptive process models are those process models that take into account factors that require flexibility in processes. This includes, for example, the context of processes or the actors involved in the process

UndoPort: Exploring the Influence of Undo-Actions for Locomotion in Virtual Reality on the Efficiency, Spatial Understanding and User Experience

When we get lost in Virtual Reality (VR) or want to return to a previous location, we use the same methods of locomotion for the way back as for the way forward. This is time-consuming and requires additional physical orientation changes, increasing the risk of getting tangled in the headsets' cables. In this paper, we propose the use of undo actions to revert locomotion steps in VR. We explore eight different variations of undo actions as extensions of point\&teleport, based on the possibility to undo position and orientation changes together with two different visualizations of the undo step (discrete and continuous). We contribute the results of a controlled experiment with 24 participants investigating the efficiency and orientation of the undo techniques in a radial maze task. We found that the combination of position and orientation undo together with a discrete visualization resulted in the highest efficiency without increasing orientation errors.Comment: To appear in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI 23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 15 page

Self-assembly of gold nanoparticles at the oil-vapor interface: from mono- to multilayers

Alkylthiol-coated gold nanoparticles spontaneously segregate from dispersion in toluene to the toluene-vapor interface. We show that surface tension drops during segregation with a rate that depends on particle concentration. Mono- and multilayers of particles form depending on particle concentration, time, and temperature. X-ray reflectometry indicates fast monolayer formation and slow multilayer formation. A model that combines diffusion-limited segregation driven by surface energy and heterogeneous agglomeration driven by dispersive van der Waals particle interactions is proposed to describe film formation

Non-ergodicity in reinforcement learning: robustness via ergodicity transformations

Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the real world. A significant challenge hindering the adoption of RL methods in these domains is the non-robustness of conventional algorithms. In this paper, we argue that a fundamental issue contributing to this lack of robustness lies in the focus on the expected value of the return as the sole ``correct'' optimization objective. The expected value is the average over the statistical ensemble of infinitely many trajectories. For non-ergodic returns, this average differs from the average over a single but infinitely long trajectory. Consequently, optimizing the expected value can lead to policies that yield exceptionally high returns with probability zero but almost surely result in catastrophic outcomes. This problem can be circumvented by transforming the time series of collected returns into one with ergodic increments. This transformation enables learning robust policies by optimizing the long-term return for individual agents rather than the average across infinitely many trajectories. We propose an algorithm for learning ergodicity transformations from data and demonstrate its effectiveness in an instructive, non-ergodic environment and on standard RL benchmarks

TicTacToes: Assessing Toe Movements as an Input Modality

From carrying grocery bags to holding onto handles on the bus, there are a variety of situations where one or both hands are busy, hindering the vision of ubiquitous interaction with technology. Voice commands, as a popular hands-free alternative, struggle with ambient noise and privacy issues. As an alternative approach, research explored movements of various body parts (e.g., head, arms) as input modalities, with foot-based techniques proving particularly suitable for hands-free interaction. Whereas previous research only considered the movement of the foot as a whole, in this work, we argue that our toes offer further degrees of freedom that can be leveraged for interaction. To explore the viability of toe-based interaction, we contribute the results of a controlled experiment with 18 participants assessing the impact of five factors on the accuracy, efficiency and user experience of such interfaces. Based on the findings, we provide design recommendations for future toe-based interfaces.Comment: To appear in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI 23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 17 page