Universität Bielefeld Play Search

Title

39xxxx Lecturer Semester Time and room ekvv

Short Description

Let's learn some algorithms for game AI, implement them, and compete against each other!

Game Code

Game: Code Runner. Code: ZIP

Tournament Results

Schedule

Date Topic
13.04.2016 Organisation of the seminar
20.04.2016 Game Code explanation, Path Finding and Breath-First Search
27.04.2016 Overview of AI for games, Minimax algorithms
04.05.2016 Alpha-beta pruning; Negamax
11.05.2016 Tournament 1 - tatics discussion
19.05.2016 (no class)
25.05.2016 Pedro Feijao - Mastering the game of Go with deep neural networks and tree search
01.06.2016 Sayandev Paul- Solving heads-up limit Texas hold’em
08.06.2016 (Pedro is travelling)
15.06.2016 Annalena Kruse - A Survey of Monte Carlo Tree Search Methods
22.06.2016 Tizian Schulz - AIs for Dominion Using Monte-Carlo Tree Search
29.06.2016 Kevin Lamkiewicz - Solving Kalah
06.07.2016 Dominik Gründing - Nearly Optimal Minimax Tree Search / Igor - TBA
13.07.2016 Philipp Herwald - Learning Cooperative Games
20.07.2016 Marius Krause - Sokoban: Improving the search with relevance cuts

Selected Papers

  • Sayandev Paul: Tammelin, O., Burch, N., Johanson, M., & Bowling, M. (2015). Solving heads-up limit Texas hold’em. IJCAI International Joint Conference on Artificial Intelligence, 2015-January(Ijcai), 645–652.
  • Philipp Herwald: Balcan, M., Procaccia, A. D., & Zick, Y. (2015). Learning Cooperative Games. Ijcai, (Ijcai), 475–481.
  • Marius Krause: Junghanns, A., & Schaeffer, J. (2001). Sokoban: Improving the search with relevance cuts. Theoretical Computer Science, 252(1-2), 151–175. http://doi.org/10.1016/S0304-3975(00)00080-3
  • Tizian Schulz: Robin Tollisen, Jon Vegard Jansen, Morten Goodwin, and S. G. (2015). AIs for Dominion Using Monte-Carlo Tree Search. Lecture Notes in Computer Science (Vol. 9101). http://doi.org/10.1007/978-3-319-19066-2
  • Anna-Lena Kruse: Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., … Colton, S. (2012). A Survey of Monte Carlo Tree Search Methods. Computational Intelligence and AI in Games, IEEE Transactions on, 4(1), 1–43. http://doi.org/10.1109/TCIAIG.2012.2186810

References for Seminars

  • Van den Herik, H. J., Uiterwijk, J. W. H. M., & Van Rijswijck, J. (2002). Games solved: Now and in the future. Artificial Intelligence, 134(1-2), 277–311. http://doi.org/10.1016/S0004-3702(01)00152-7
  • Schaeffer, J., & Van den Herik, H. J. (2002). Games, computers, and artificial intelligence. Artificial Intelligence, 134(1-2), 1–7. http://doi.org/10.1016/S0004-3702(01)00165-5
  • Buro, M. (2002). Improving heuristic mini-max search by supervised learning. Artificial Intelligence, 134(1-2), 85–99. http://doi.org/10.1016/S0004-3702(01)00093-5
  • Schaeffer, J., Burch, N., Björnsson, Y., Kishimoto, A., Müller, M., Lake, R., … Sutphen, S. (2007). Checkers is solved. Science (New York, N.Y.), 317(5844), 1518–1522. http://doi.org/10.1126/science.1144079
  • Bjornsson, Y., & Finnsson, H. (2009). CadiaPlayer: A simulation-based general game player. IEEE Transactions on Computational Intelligence and AI in Games, 1(1), 4–15. http://doi.org/10.1109/TCIAIG.2009.2018702
  • Möller, M., Schneider, M., Wegner, M., & Schaub, T. (2011). Centurio, a general game player: Parallel, Java-and ASP-based. KI-Kunstliche Intelligenz, 25(1), 17–24. http://doi.org/10.1007/s13218-010-0077-4
  • Whitehouse, D., Cowling, P. I., Powley, E. J., & Rollason, J. (2013). Integrating Monte Carlo Tree Search with Knowledge-Based Methods to Create Engaging Play in a Commercial Mobile Game. Proc. Artif. Intell. Interact. Digital Entert. Conf., 100–106.
  • Lanctot, M., Saffidine, A., Veness, J., Archibald, C., & Winands, M. H. M. (2013). Monte Carlo *-Minimax Search. In 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013). August 3-9, 2013, Beijing, China. (pp. 1–16). Retrieved from http://arxiv.org/abs/1304.6057
  • Whitehouse, D. (2014). Monte Carlo Tree Search for games with Hidden Information and Uncertainty.
  • Plaat, A., Schaeffer, J., Pijls, W., & de Bruin, A. (2014). Nearly Optimal Minimax Tree Search? CoRR, abs/1404.1. Retrieved from http://arxiv.org/abs/1404.1518
  • Plaat, A., Schaeffer, J., Pijls, W., & de Bruin, A. (2014). SSS * = Alpha-Beta + {TT}. CoRR, abs/1404.1. Retrieved from http://arxiv.org/abs/1404.1517
  • Brânzei, S., & Miltersen, P. B. (2015). A dictatorship theorem for cake cutting. IJCAI International Joint Conference on Artificial Intelligence, 2015-January(Ijcai), 482–488.
  • Dann, M., Michaeldannrmiteduau, E., Zambetta, F., Fabiozambettarmiteduau, E., Thangarajah, J., Zambettau, F., & Thangarajah, J. (2015). An improved approach to reinforcement learning in Computer Go. 2015 IEEE Conference on Computational Intelligence and Games (CIG), 169–176. http://doi.org/10.1109/CIG.2015.7317910
  • Cowling, P. I., Whitehouse, D., & Powley, E. J. (2015). Emergent bluffing and inference with Monte Carlo Tree Search, 114–121.
  • Kroer, C., & Sandholm, T. (2015). Limited lookahead in imperfect-information games. IJCAI International Joint Conference on Artificial Intelligence, 2015-January(Ijcai), 575–581.
  • Peters, D., & Elkind, E. (2015). Simple causes of complexity in hedonic games. IJCAI International Joint Conference on Artificial Intelligence, 2015-January(Ijcai), 617–623.
  • Heinrich, J., & Silver, D. (2015). Smooth UCT search in computer poker. IJCAI International Joint Conference on Artificial Intelligence, 2015-January(Ijcai), 554–560.
  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., … Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. http://doi.org/10.1038/nature16961
  • Plaat, A., Schaeffer, J., Pijls, W., & Bruin, A. De. (n.d.). Best-First and Depth-First Minimax Search in Practice.

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