Faults that occur in an autonomous robot system negatively affect its dependability. The aim of truly dependable and autonomous systems requires that one has to deal with these faults in some way. In order to be able to do this efficiently one has to have information on the nature of these faults. Very few studies on this topic have been conducted so far. In this paper we present results of a survey on faults of autonomous robots we conducted in the context of RoboCup. The major contribution of this paper is twofold. First we present an adapted fault taxonomy suitable for autonomous robots. Second we give information on the nature, the relevance and impact of faults in robot systems that are beneficial for researcher dealing with fault mitigation and management in autonomous systems.
Profound knowledge about Artificial Intelligence (AI) will become increasingly important for careers in science and engineering. Therefore an innovative educational project teaching fundamental concepts of AI at high school level will be presented in this paper. We developed an AI-course covering major topics (problem solving, search, planning, graphs, datastructures, automata, agent systems, machine learning) which comprises both theoretical and hands-on components. A pilot project was conducted and empirically evaluated. Results of the evaluation show that the participating pupils have become familiar with those concepts and the various topics addressed. Results and lessons learned from this project form the basis for further projects in different schools which intend to integrate AI in future secondary science education.
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