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We address the problem of guiding a robot in such a way, that it can decide, based on perceived sensor data, which future actions to choose, in order to reach a goal. In order to realize this guidance, the robot has access to a (probabilistic) automaton (PA), whose nal states represent concepts, which have to be recognized in order to verify, that a goal(More)
We present methods for optimizing chain Datalog programs by restructuring and post-processing. The rules of the programs deene intensionally a set of target concepts, which are to be derived via forward chaining. The restructuring methods transform the rules, such that redundancies and ambiguities, which p r e v ent ecient e v aluations, are removed without(More)
Machine learningcan be a most valuable tool for improvingthe exibilityand eeciency of robot applications. Many approachesto applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the(More)
The application of logic-based learning algorithms in real-world domains, such as robotics, requires extensive data engineering, including the transformation of numerical tabular representations of real-world data to logic-based representations, feature and concept selection, the generation of the respective descriptions, and the composition of training and(More)
Mobile telemedical applications are of crucial importance today as they do offer the potential to improve the quality of the health care related services. It has been proven that the leading cause of several illnesses and diseases are stress and the lack of fitness practices. Based on that, a system capable of estimating and monitoring both stress and(More)
Machine learning can ooer an increase in the exibility and applicability of robotics at several levels of control. In this paper, we characterize two symbolic learning tasks in the eld of robotics. We outline an approach for learning features from sensory data and for using these features to learn more complex ones. We illustrate our approach with rst(More)