| Evaluating Real-Time Strategy Game States Using Convolutional Neural Networks M Stanescu, NA Barriga, A Hess, M Buro IEEE Computational Intelligence in Games 2016, 2016 | 48 | 2016 |
| Predicting army combat outcomes in StarCraft M Stanescu, SP Hernandez, G Erickson, R Greiner, M Buro Proceedings of the AAAI Conference on Artificial Intelligence and …, 2013 | 44 | 2013 |
| Combining strategic learning with tactical search in real-time strategy games N Barriga, M Stanescu, M Buro Proceedings of the AAAI Conference on Artificial Intelligence and …, 2017 | 36 | 2017 |
| Hierarchical adversarial search applied to real-time strategy games M Stanescu, N Barriga, M Buro Proceedings of the AAAI Conference on Artificial Intelligence and …, 2014 | 35 | 2014 |
| Game tree search based on nondeterministic action scripts in real-time strategy games NA Barriga, M Stanescu, M Buro IEEE Transactions on Games 10 (1), 69-77, 2017 | 32 | 2017 |
| Puppet search: Enhancing scripted behavior by look-ahead search with applications to real-time strategy games N Barriga, M Stanescu, M Buro Proceedings of the AAAI Conference on Artificial Intelligence and …, 2015 | 28 | 2015 |
| Using Lanchester attrition laws for combat prediction in StarCraft M Stanescu, N Barriga, M Buro Proceedings of the AAAI Conference on Artificial Intelligence and …, 2015 | 23 | 2015 |
| Predicting Opponent’s Production in Real-Time Strategy Games with Answer Set Programming M Stanescu, M Certicky IEEE Transactions on Computational Intelligence and AI in Games, 2014 | 23 | 2014 |
| Building Placement Optimization in Real-Time Strategy Games NA Barriga, M Stanescu, M Buro Tenth Annual AAAI Conference on Artificial Intelligence and Interactive …, 2014 | 16 | 2014 |
| Parallel UCT search on GPUs NA Barriga, M Stanescu, M Buro 2014 IEEE Conference on Computational Intelligence and Games, 1-7, 2014 | 12 | 2014 |
| Rating systems with multiple factors M Stanescu Master's thesis, School of Informatics, Univ. of Edinburgh, Edinburgh, UK, 2011 | 11 | 2011 |
| Improving rts game ai by supervised policy learning, tactical search, and deep reinforcement learning NA Barriga, M Stanescu, F Besoain, M Buro IEEE Computational Intelligence Magazine 14 (3), 8-18, 2019 | 9 | 2019 |
| Introducing Hierarchical Adversarial Search, a Scalable Search Procedure for Real-Time Strategy Games M Stanescu, NA Barriga, M Buro 21st European Conference on Artificial Intelligence 263, 1099 - 1100, 2014 | 7 | 2014 |
| Combining scripted behavior with game tree search for stronger, more robust game AI NA Barriga, M Stanescu, M Buro Game AI Pro 3: Collected Wisdom of Game AI Professionals, 179, 2017 | 6 | 2017 |
| Combat outcome prediction for real-time strategy games M Stanescu, NA Barriga, M Buro Game AI Pro 3: Collected Wisdom of Game AI Professionals, 301, 2017 | 4 | 2017 |
| Outcome Prediction and Hierarchical Models in Real-Time Strategy Games M Stanescu PhD Thesis, University of Alberta, Canada, 2019 | 1 | 2019 |
| Spatial Action Decomposition Learning Applied to RTS Combat Games M Stanescu, M Buro Proceedings of the AIIDE 2018 Workshop on Artificial Intelligence for …, 2018 | 1 | 2018 |
| The Quo Vadis submission at Traffic4cast 2019 D Oneata, CG Alexandru, M Stanescu, O Pascu, A Magan, A Postelnicu, ... arXiv preprint arXiv:1910.12363, 2019 | | 2019 |
| Improving RTS Game AI by Supervised Policy Learning, Tactical Search, and Deep Reinforcement Learning M Stanescu, M Buro | | |
| Hierarchical Adversarial Search and its Application to Real-Time Strategy Games M Stanescu, NA Barriga, M Buro | | |
Nicolas A. BarrigaUniversidad de TalcaVerified email at utalca.cl
