TY - JOUR A1 - Weichert, Dorina A1 - Kister, Alexander A1 - Volbach, Peter A1 - Houben, Sebastian A1 - Trost, Marcus A1 - Wrobel, Stefan T1 - Explainable production planning under partial observability in high-precision manufacturing N2 - Conceptually, high-precision manufacturing is a sequence of production and measurement steps, where both kinds of steps require to use non-deterministic models to represent production and measurement tolerances. This paper demonstrates how to effectively represent these manufacturing processes as Partially Observable Markov Decision Processes (POMDP) and derive an offline strategy with state-of-the-art Monte Carlo Tree Search (MCTS) approaches. In doing so, we face two challenges: a continuous observation space and explainability requirements from the side of the process engineers. As a result, we find that a tradeoff between the quantitative performance of the solution and its explainability is required. In a nutshell, the paper elucidates the entire process of explainable production planning: We design and validate a white-box simulation from expert knowledge, examine state-of-the-art POMDP solvers, and discuss our results from both the perspective of machine learning research and as an illustration for high-precision manufacturing practitioners. KW - Explainability KW - Manufacturing KW - Reinforcement Learning KW - Monte Carlo tree search KW - Partially observable Markov decision process PY - 2023 DO - https://doi.org/10.1016/j.jmsy.2023.08.009 SN - 0278-6125 VL - 70 SP - 514 EP - 524 PB - Elsevier Ltd. CY - Southfield AN - OPUS4-58963 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Weichert, Dorina A1 - Kister, Alexander A1 - Houben, Sebastian A1 - Link, Patrick A1 - Ernis, Gunar ED - Kiyavash, Negar ED - Mooij, Joris M. T1 - Robust Entropy Search for Safe Efficient Bayesian Optimization N2 - The practical use of Bayesian Optimization (BO) in engineering applications imposes special requirements: high sampling efficiency on the one hand and finding a robust solution on the other hand. We address the case of adversarial robustness, where all parameters are controllable during the optimization process, but a subset of them is uncontrollable or even adversely perturbed at the time of application. To this end, we develop an efficient information-based acquisition function that we call Robust Entropy Search (RES). We empirically demonstrate its benefits in experiments on synthetic and real-life data. The results show that RES reliably finds robust optima, outperforming state-of-the-art algorithms. T2 - UAI 2024 CY - Barcelona, Spain DA - 15.07.2024 KW - Bayesian Optimization KW - Gaussian process KW - Active learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613523 UR - https://proceedings.mlr.press/v244/weichert24a.html SN - 2640-3498 VL - 244 SP - 3711 EP - 3729 PB - Proceedings of Machine Learning Research AN - OPUS4-61352 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -