Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Minimax real-time heuristic search
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Domain-Independent Online Planning for STRIPS Domains
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
LICS '96 Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science
Mechatronic Systems: Fundamentals
Mechatronic Systems: Fundamentals
Accurate diagnosis of induction machine faults using optimal time-frequency representations
Engineering Applications of Artificial Intelligence
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Modelling mixed discrete-continuous domains for planning
Journal of Artificial Intelligence Research
Online planning algorithms for POMDPs
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Fuzzy logic-based decision-making for fault diagnosis in a DC motor
Engineering Applications of Artificial Intelligence
Adaptive neural network model based predictive control for air-fuel ratio of SI engines
Engineering Applications of Artificial Intelligence
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
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Mechatronic systems are a relatively new class of technical systems. The integration of electro-mechanical systems with hard- and software enables systems that adapt to changing operation conditions and externally defined objective functions. To gain superior system performance from this ability, sophisticated decision making processes are required. Planning is an ideal method to integrate long-term considerations beyond the time horizon of classical controlled systems into the decision making process. Unfortunately, planning employs discrete models, while mechatronic systems or controlled systems in general emphasize the time continuous behavior of processes. As a result, deviations of the actual behavior during the execution from the planned behavior plan cannot be entirely avoided. We introduce a hybrid planning architecture, which combines planning and learning from artificial intelligence with simulation techniques to optimize the general system behavior. The presented approach is able to handle the inevitable deviations during plan execution, and thus maintains feasibility and quality of the created plans.