Researcher

Winands, M.H.M.

Professor

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  1. 2019
  2. Published
  3. Published
    Drageset, O., Winands, M. H. M., Gaina, R. D., & Perez-Liebana, D. (2019). Optimising Level Generators for General Video Game AI. In 2019 IEEE Conference on Games (CoG) (pp. 1-8). IEEE. https://doi.org/10.1109/CIG.2019.8847961
  4. Published
  5. Published
  6. Published
  7. Published
  8. 2018
  9. Published
    Siwek, C., Kowalski, J., Sironi, C. F., & Winands, M. H. M. (2018). Implementing Propositional Networks on FPGA. In AI 2018: Advances in Artificial Intelligence. (pp. 133-145). (Lecture Notes in Computer Science; Vol. 11320). Cham: Springer. https://doi.org/10.1007/978-3-030-03991-2_14
  10. Published
  11. Published
    Browne, C., Winands, M., Liu, J., & Preuss, M. (2018). CIG 2018 Preface. In 2018 IEEE Conference on Computational Intelligence and Games (pp. iii). IEEE. https://doi.org/10.1109/CIG.2018.8490408
  12. Published
    Rooijackers, M. L. M., & Winands, M. H. M. (2018). Wall Building in the Game of StarCraft with Terrain Considerations. In 2018 IEEE Conference on Computational Intelligence and Games (pp. 453-457). IEEE. https://doi.org/10.1109/CIG.2018.8490413
  13. Published
    Baier, H., & Winands, M. H. M. (2018). MCTS-Minimax Hybrids with State Evaluations (Extended Abstract). 5548-5552. Abstract from Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden. https://doi.org/10.24963/ijcai.2018/782
  14. Published
    Gaina, R. D., Couëtoux, A., Soemers, D. J. N. J., Winands, M. H. M., Vodopivec, T., Kirchgessner, F., ... Diego Perez-Liebana, D. (2018). The 2016 Two-Player GVGAI Competition. IEEE Transactions on Games, 10(2), 209-220. https://doi.org/10.1109/TCIAIG.2017.2771241
  15. Published
  16. Published
    Soemers, D. J. N. J., Brys, T., Driessens, K., Winands, M. H. M., & Nowé, A. (2018). Adapting to Concept Drift in Credit Card Transaction Data Streams Using Contextual Bandits and Decision Trees. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018: The Thirtieth AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-18) (pp. 7831-7836). AAAI Press.
  17. Published
  18. Published
    Sironi, C. F., & Winands, M. H. M. (2018). On-Line Parameter Tuning for Monte-Carlo Tree Search in General Game Playing. In T. Cazenave, M. H. M. Winands, & A. Saffidine (Eds.), Computer Games: 6th Workshop, CGW 2017, Held in Conjunction with the 26th International Conference on Artificial Intelligence, IJCAI 2017, Melbourne, VIC, Australia, August, 20, 2017, Revised Selected Papers (pp. 75-95). Cham: Springer. https://doi.org/10.1007/978-3-319-75931-9_6
  19. Published
    Sironi, C. F., & Winands, M. H. M. (2018). On-Line Parameter Tuning for Monte-Carlo Tree Search in General Game Playing. 235-236. Abstract from 30th Benelux Conference on Artificial Intelligence
    , Den Bosch, Netherlands.
  20. Published
    Sironi, C. F., Liu, J., Perez-Liebana, D., Gaina, R. D., Bravi, I., Lucas, S. M., & Winands, M. H. M. (2018). Self-adaptive MCTS for General Video Game Playing. In K. Sim, & P. Kaufmann (Eds.), Applications of Evolutionary Computation. EvoApplications 2018 (Vol. 10784, pp. 358-375). (Lecture Notes in Computer Science; Vol. 10784). Springer. https://doi.org/10.1007/978-3-319-77538-8_25
  21. 2017
  22. Published
    Rooijackers, M. L. M., & Winands, M. H. M. (2017). Resource-gathering algorithms in the game of Starcraft. In 2017 IEEE Conference on Computational Intelligence and Games (pp. 264-271). IEEE. https://doi.org/10.1109/CIG.2017.8080445
  23. Published
    Sironi, C. F., & Winands, M. H. M. (2017). Optimizing Propositional Networks. In Computer Games. CGW 2016, GIGA 2016 (pp. 133-151). [Chapter 10] ( Communications in Computer and Information Science; Vol. 705). Springer. https://doi.org/10.1007/978-3-319-57969-6_10
  24. Published
  25. Published
  26. Published
  27. Published
  28. Published
    Winands, M. H. M. (2017). Monte-Carlo Tree Search in Board Games. In R. Nakatsu, M. Rauterberg, & P. Ciancarini (Eds.), Handbook of Digital Games and Entertainment Technologies (pp. 47-76). [Chapter 3] Springer. https://doi.org/10.1007/978-981-4560-50-4_27
  29. Published
  30. 2016
  31. Published
  32. Published
  33. Published
    Soemers, D. J. N. J., & Winands, M. H. M. (2016). Hierarchical Task Network Plan Reuse for video games. In 2016 IEEE Conference on Computational Intelligence and Games (CIG) (pp. 1-8). IEEE. https://doi.org/10.1109/CIG.2016.7860395
  34. Published
  35. Published
  36. Published
  37. Published
    Winands, M. H. M. (2016). The Surakarta Bot Revealed. In Computer Games (pp. 71-82). [Chapter 6] (Communications in Computer and Information Science; Vol. 614). Springer. https://doi.org/10.1007/978-3-319-39402-2_6
  38. Published
  39. Published
    Pepels, T., Cazenave, T., & Winands, M. H. M. (2016). Sequential halving for partially observable games. In Computer Games (pp. 16-29). (Communications in Computer and Information Science; Vol. 614). Springer. https://doi.org/10.1007/978-3-319-39402-2_2
  40. 2015
  41. Published
  42. Published
    Winands, M. H. M. (2015). Monte-Carlo Tree Search. In N. Lee (Ed.), Encyclopedia of Computer Graphics and Games (pp. 1-6). Springer. https://doi.org/10.1007/978-3-319-08234-9_12-1
  43. Published
    Winands, M. (2015). Neural Networks for Video Game AI. (Dagstuhl Reports; Vol. 5, No. 1). Schloss Dagstuhl.
  44. 2014
  45. Published
  46. Published
    Tak, M. J. W., Lanctot, M., & Winands, M. H. M. (2014). Monte Carlo Tree Search variants for simultaneous move games. In IEEE Conference on Computatonal Intelligence and Games, CIG 2014 (pp. 232-239). IEEE Computer Society. https://doi.org/10.1109/CIG.2014.6932889
  47. Published
    Lanctot, M., Winands, M. H. M., Pepels, T., & Sturtevant, N. R. (2014). Monte carlo tree search with heuristic evaluations using implicit minimax backups. In IEEE Conference on Computatonal Intelligence and Games, CIG (pp. 341-348). IEEE Computer Society. https://doi.org/10.1109/CIG.2014.6932903
  48. Published
  49. Published
    Esser, M., Gras, M., Winands, M. H. M., Schadd, M. P. D., & Lanctot, M. (2014). Improving Best-Reply Search. In Computers and Games (pp. 125-137). [Chapter 11] (Lecture Notes in Computer Science; Vol. 8427). Springer. https://doi.org/10.1007/978-3-319-09165-5_11
  50. Published
  51. Published
  52. Published
  53. Published
    Pepels, T., Cazenave, T., Winands, M. H. M., & Lanctot, M. (2014). Minimizing Simple and Cumulative Regret in Monte-Carlo Tree Search. In Computer Games: Third Workshop on Computer Games, CGW 2014, Held in Conjunction with the 21st European Conference on Artificial Intelligence, ECAI 2014, Prague, Czech Republic, August 18, 2014, Revised Selected Papers (pp. 1-15). (Communications in Computer and Information Science; Vol. 504). Springer. https://doi.org/10.1007/978-3-319-14923-3_1
  54. Published
    Lanctot, M., Lisý, V., & Winands, M. H. M. (2014). Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel. In Computer Games: Workshop on Computer Games, CGW 2013, Held in Conjunction with the 23rd International Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, August 3, 2013, Revised Selected Papers (pp. 28-43). (Communications in Computer and Information Science; Vol. 408). Springer. https://doi.org/10.1007/978-3-319-05428-5_3
  55. Published
    Baier, H., & Winands, M. H. M. (2014). Monte-Carlo Tree Search and Minimax Hybrids with Heuristic Evaluation Functions. In Computer Games: Third Workshop on Computer Games, CGW 2014, Held in Conjunction with the 21st European Conference on Artificial Intelligence, ECAI 2014, Prague, Czech Republic, August 18, 2014, Revised Selected Papers (pp. 45-63). (Communications in Computer and Information Science; Vol. 504). Springer. https://doi.org/10.1007/978-3-319-14923-3_4
  56. Published
    Pepels, T., Tak, M. J. W., Lanctot, M., & Winands, M. H. M. (2014). Quality-based Rewards for Monte-Carlo Tree Search Simulations. In Proceedings of the Twenty-first European Conference on Artificial Intelligence (pp. 705-710). ( Frontiers in Artificial Intelligence and Applications; Vol. 263). Amsterdam, The Netherlands, The Netherlands: IOS Press. https://doi.org/10.3233/978-1-61499-419-0-705
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