Alexander Förster

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The Unified Modeling Language (UML)1 is a visual, object-oriented, and multipurpose modeling language. Primarily designed for modeling software systems, it can also be used for business process modeling. Since the early 1970s, a large variety of languages for data and software modeling like entity-relationship diagrams [2], message sequence charts [5, 10],(More)
Swarm robotics systems are characterized by decentralized control, limited communication between robots, use of local information, and emergence of global behavior. Such systems have shown their potential for flexibility and robustness [1]-[3]. However, existing swarm robotics systems are by and large still limited to displaying simple proof-of-concept(More)
Business-driven development favors the construction of process models at different abstraction levels and by different people. As a consequence, there is a demand for consolidating different versions of process models by detecting and resolving differences. Existing approaches rely on the existence of a change log which logs the changes when changing a(More)
This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov decision problems (POMDPs) that require long-term memories of past observations. The approach involves approximating a policy gradient for a Recurrent Neural Network (RNN) by(More)
Reinforcement learning for partially observable Markov decision problems (POMDPs) is a challenge as it requires policies with an internal state. Traditional approaches suffer significantly from this shortcoming and usually make strong assumptions on the problem domain such as perfect system models, state-estimators and a Markovian hidden system. Recurrent(More)
We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of(More)
Automatically classifying terrain such as rocks, sand and gravel from images is a challenging machine vision problem. In addition to human designed approaches, a great deal of progress has been made using machine learning techniques to perform classification from images. In this work, we demonstrate the first known use of Cartesian Genetic Programming (CGP)(More)
We describe a new algorithm for robot localization, efficient both in terms of memory and processing time. It transforms a stream of laser range sensor data into a probabilistic calculation of the robot’s position, using a bidirectional Long Short-Term Memory (LSTM) recurrent neural network (RNN) to learn the structure of the environment and to answer(More)
Business processes usually have to consider certain constraints like domain specific and quality requirements. The automated formal verification of these constraints is desirable, but requires the user to provide an unambiguous formal specification. In particular since the notations for business process modeling are usually visual flow-oriented languages,(More)
Node clustering and data aggregation are popular techniques to reduce energy consumption in large WSNs and a large body of literature has emerged describing various clustering protocols. Unfortunately, for practitioners wishing to exploit clustering in deployments, there is little help when trying to identify a protocol that meets their needs. This paper(More)