%0 Conference Paper %1 Baier2023 %A Baier, Alexandra %A Aspandi, Decky %A Staab, Steffen %B Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China %D 2023 %I International Joint Conferences on Artificial Intelligence Organization %K ac-inmotion from:alexbaier myown %P 3461--3469 %R 10.24963/IJCAI.2023/385 %T ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks %U https://doi.org/10.24963/ijcai.2023/385 %X Multistep prediction models are essential for the simulation and model-predictive control of dynamical systems. Verifying the safety of such models is a multi-faceted problem requiring both system-theoretic guarantees as well as establishing trust with human users. In this work, we propose a novel approach, ReLiNet (Recurrent Linear Parameter Varying Network), to ensure safety for multistep prediction of dynamical systems. Our approach simplifies a recurrent neural network to a switched linear system that is constrained to guarantee exponential stability, which acts as a surrogate for safety from a system-theoretic perspective. Furthermore, ReLiNet’s computation can be reduced to a single linear model for each time step, resulting in predictions that are explainable by definition, thereby establishing trust from a human-centric perspective. Our quantitative experiments show that ReLiNet achieves prediction accuracy comparable to that of state-of-the-art recurrent neural networks, while achieving more faithful and robust explanations compared to the model-agnostic explanation method of LIME.

@inproceedings{Baier2023, abstract = {Multistep prediction models are essential for the simulation and model-predictive control of dynamical systems. Verifying the safety of such models is a multi-faceted problem requiring both system-theoretic guarantees as well as establishing trust with human users. In this work, we propose a novel approach, ReLiNet (Recurrent Linear Parameter Varying Network), to ensure safety for multistep prediction of dynamical systems. Our approach simplifies a recurrent neural network to a switched linear system that is constrained to guarantee exponential stability, which acts as a surrogate for safety from a system-theoretic perspective. Furthermore, ReLiNet’s computation can be reduced to a single linear model for each time step, resulting in predictions that are explainable by definition, thereby establishing trust from a human-centric perspective. Our quantitative experiments show that ReLiNet achieves prediction accuracy comparable to that of state-of-the-art recurrent neural networks, while achieving more faithful and robust explanations compared to the model-agnostic explanation method of LIME.}, added-at = {2023-06-13T10:22:08.000+0200}, author = {Baier, Alexandra and Aspandi, Decky and Staab, Steffen}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2ed8737e44fb8d97aa07ec64c776a2a81/analyticcomp}, booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI} 2023, 19th-25th August 2023, Macao, SAR, China}, doi = {10.24963/IJCAI.2023/385}, interhash = {2e996c3803dd3030b150002c1cabf6a1}, intrahash = {ed8737e44fb8d97aa07ec64c776a2a81}, keywords = {ac-inmotion from:alexbaier myown}, month = aug, note = {Main Track}, pages = {3461--3469}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, timestamp = {2024-03-15T11:35:07.000+0100}, title = {ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks}, url = {https://doi.org/10.24963/ijcai.2023/385}, year = 2023 }