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
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
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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