6 papers
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
Jonas Ehrhardt, René Heesch, Oliver Niggemann
In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters.…
Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark
Felix Leonhard Janzen, Lukas Moddemann, Alexander Diedrich +1
In many cyber-physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is lacking due to data protection i…
Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information
Henrik Sebastian Steude, Alexander Diedrich, Ingo Pill +3
Diagnostic processes for complex cyber-physical systems often require extensive prior knowledge in the form of detailed system models or comprehensive training data. However, obtai…
AAAI Workshop on AI Planning for Cyber-Physical Systems -- CAIPI24
Oliver Niggemann, Gautam Biswas, Alexander Diedrich +3
The workshop 'AI-based Planning for Cyber-Physical Systems', which took place on February 26, 2024, as part of the 38th Annual AAAI Conference on Artificial Intelligence in Vancouv…
Diagnosis driven Anomaly Detection for CPS
Henrik S. Steude, Lukas Moddemann, Alexander Diedrich +2
In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isola…
Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
Lukas Moddemann, Henrik Sebastian Steude, Alexander Diedrich +1
Consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts, especially for dynamic multi-modal time se…