| Introducing the hearthstone-ai competition A Dockhorn, S Mostaghim arXiv preprint arXiv:1906.04238, 2019 | 40* | 2019 |
| Forward model approximation for general video game learning A Dockhorn, D Apeldoorn 2018 IEEE Conference on Computational Intelligence and Games (CIG), 1-8, 2018 | 30 | 2018 |
| TAG: A tabletop games framework R Gaina, M Balla, A Dockhorn, R Montoliu Colás CEUR Workshop Proceedings, 2020 | 28 | 2020 |
| An alternating optimization approach based on hierarchical adaptations of dbscan A Dockhorn, C Braune, R Kruse 2015 IEEE Symposium Series on Computational Intelligence, 749-755, 2015 | 27 | 2015 |
| A local approach to forward model learning: Results on the game of life game SM Lucas, A Dockhorn, V Volz, C Bamford, RD Gaina, I Bravi, ... 2019 IEEE Conference on Games (CoG), 1-8, 2019 | 23 | 2019 |
| Predicting opponent moves for improving hearthstone ai A Dockhorn, M Frick, Ü Akkaya, R Kruse International Conference on Information Processing and Management of …, 2018 | 23 | 2018 |
| Choosing representation, mutation, and crossover in genetic algorithms A Dockhorn, S Lucas IEEE Computational Intelligence Magazine 17 (4), 52-53, 2022 | 17 | 2022 |
| STRATEGA: A General Strategy Games Framework. A Dockhorn, JH Grueso, D Jeurissen, DP Liebana AIIDE Workshops, 2020 | 17 | 2020 |
| Generating diverse and competitive play-styles for strategy games D Perez-Liebana, C Guerrero-Romero, A Dockhorn, L Xu, J Hurtado, ... 2021 IEEE Conference on Games (CoG), 1-8, 2021 | 16 | 2021 |
| Learning local forward models on unforgiving games A Dockhorn, SM Lucas, V Volz, I Bravi, RD Gaina, D Perez-Liebana 2019 IEEE Conference on Games (CoG), 1-4, 2019 | 15 | 2019 |
| Variable density based clustering A Dockhorn, C Braune, R Kruse 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2016 | 14 | 2016 |
| Autorl hyperparameter landscapes A Mohan, C Benjamins, K Wienecke, A Dockhorn, M Lindauer arXiv preprint arXiv:2304.02396, 2023 | 12 | 2023 |
| Design and implementation of TAG: a tabletop games framework RD Gaina, M Balla, A Dockhorn, R Montoliu, D Perez-Liebana arXiv preprint arXiv:2009.12065, 2020 | 12 | 2020 |
| Exception-tolerant hierarchical knowledge bases for forward model learning D Apeldoorn, A Dockhorn IEEE Transactions on Games 13 (3), 249-262, 2020 | 12 | 2020 |
| A decision heuristic for Monte Carlo tree search doppelkopf agents A Dockhorn, C Doell, M Hewelt, R Kruse 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2017 | 12 | 2017 |
| Portfolio search and optimization for general strategy game-playing A Dockhorn, J Hurtado-Grueso, D Jeurissen, L Xu, D Perez-Liebana 2021 IEEE Congress on Evolutionary Computation (CEC), 2085-2092, 2021 | 10 | 2021 |
| Game state and action abstracting monte carlo tree search for general strategy game-playing A Dockhorn, J Hurtado-Grueso, D Jeurissen, L Xu, D Perez-Liebana 2021 IEEE Conference on Games (CoG), 1-8, 2021 | 9 | 2021 |
| Model decomposition for forward model approximation A Dockhorn, T Tippelt, R Kruse 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 1751-1757, 2018 | 9 | 2018 |
| Combining cooperative and adversarial coevolution in the context of pac-man A Dockhorn, R Kruse 2017 IEEE Conference on Computational Intelligence and Games (CIG), 60-67, 2017 | 9 | 2017 |
| State and action abstraction for search and reinforcement learning algorithms A Dockhorn, R Kruse Artificial Intelligence in Control and Decision-making Systems: Dedicated to …, 2023 | 8 | 2023 |
Rudolf KruseProfessor of Computer Science, Otto von Guericke University of MagdeburgBestätigte E-Mail-Adresse bei ovgu.de
