Machine intelligence for adaptable closed loop and open loop production engineering systems. - Johannes Günther 002 (2018)

Meta-learning for Predictive Knowledge Architectures: A Case Study Using TIDBD on a Sensor-rich Robotic Arm. - Johannes Günther 002; Alex Kearney; Nadia M. Ady; Michael Rory Dawson; Patrick M. Pilarski (AAMAS, 2019)

Detecting the Onset of Machine Failure Using Anomaly Detection Methods. - Mohammad Riazi; Osmar R. Zaïane; Tomoharu Takeuchi; Anthony Maltais; Johannes Günther 002; Micheal Lipsett (DaWaK, 2019)

General Dynamic Neural Networks for explainable PID parameter tuning in control engineering: An extensive comparison. - Johannes Günther 002; Elias Reichensdörfer; Patrick M. Pilarski; Klaus Diepold (CoRR, 2019)

Gamma-Nets: Generalizing Value Estimation over Timescale. - Craig Sherstan; Shibhansh Dohare; James MacGlashan; Johannes Günther 002; Patrick M. Pilarski (AAAI, 2020)

Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures. - Johannes Günther 002; Nadia M. Ady; Alex Kearney; Michael Rory Dawson; Patrick M. Pilarski (Frontiers Robotics AI, 2020)

Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making. - Alex Kearney; Anna Koop; Johannes Günther 002; Patrick M. Pilarski (CoRR, 2021)

What Should I Know? Using Meta-Gradient Descent for Predictive Feature Discovery in a Single Stream of Experience. - Alex Kearney; Anna Koop; Johannes Günther 002; Patrick M. Pilarski (CoLLAs, 2022)

Composite Recurrent Convolutional Neural Networks Offer a Position-Aware Prosthesis Control Alternative While Balancing Predictive Accuracy with Training Burden. - Heather E. Williams; Johannes Günther 002; Jacqueline S. Hebert; Patrick M. Pilarski; Ahmed W. Shehata (ICORR, 2022)

Affordance as general value function: a computational model. - Daniel Graves; Johannes Günther 002; Jun Luo 009 (Adapt. Behav., 2022)

Prototyping three key properties of specific curiosity in computational reinforcement learning. - Nadia M. Ady; Roshan Shariff; Johannes Günther 002; Patrick M. Pilarski (CoRR, 2022)

Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have. - Nadia M. Ady; Roshan Shariff; Johannes Günther 002; Patrick M. Pilarski (CoRR, 2022)

Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge. - Alex Kearney; Johannes Günther 002; Patrick M. Pilarski (Frontiers Artif. Intell., 2022)

Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance. - Mara Cairo; Bevin Eldaphonse; Payam Mousavi; Sahir; Sheikh Jubair; Matthew E. Taylor; Graham Doerksen; Nikolai Kummer; Jordan Maretzki; Gupreet Mohhar; Sean Murphy; Johannes Günther 002; et al. (AAMAS, 2023)

Explainability of deep reinforcement learning algorithms in robotic domains by using Layer-wise Relevance Propagation. - Mehran Taghian; Shotaro Miwa; Yoshihiro Mitsuka; Johannes Günther 002; Shadan Golestan; Osmar R. Zaïane (Eng. Appl. Artif. Intell., 2024)

General value functions for fault detection in multivariate time series data. - Andy Wong; Mehran Taghian Jazi; Tomoharu Takeuchi; Johannes Günther 002; Osmar Zaïane (Frontiers Robotics AI, 2024)