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Industrie 4.0 introduces decentralized, self-organizing and self-learning systems for production control. At the same time, new machine learning algorithms are getting increasingly powerful and solve real world problems. We apply Google DeepMind’s Deep Q Network (DQN) agent algorithm for Reinforcement Learning (RL) to production scheduling to achieve the Industrie 4.0 vision for production control...
We consider the question of how to treat existing, context-based test inputs when contextual conditions change. Simply ignoring the voided inputs reduces confidence in the correctness of the system under test (SuT). Instead, we suggest to adjust the parameters of those inputs to the new conditions in a way that retains their original intention. This often comprises behavioral assumptions, e.g., because...
Uniform test suites consist of test cases exclusively differing in test inputs - not in test goals. Intended to gain confidence that a given invariant holds, these inputs trigger particular behavior of the system under test. Equipped with a simulation of the system under test we are able to cheaply explore this behavior virtually. When changing over to reality, testing the system within its real context,...
Testing adaptive systems shows some similarities to playing a game against a human. We investigate this analogy and consider the differences to the game of testing traditional systems. We map the task of revealing reachable failure situations to a stochastic game. This viewpoint enables us to evaluate and find good test strategies with methods normally used by game playing machines. A reference case...
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