Title

39xxxx

Lecturer

Semester

Time and room

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

Tournament 1: https://www.dropbox.com/sh/yhd479mh4u9e8n0/AABLZtQgVS_Wm6RPuDiOeC6Ka?dl=0

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

Plaat, a. (1996). Best-first fixed-depth minimax algorithms. Artificial Intelligence, 87(1-2), 255–293. http://doi.org/10.1016/0004-3702(95)00126-3

Schaeffer, J., Lake, R., Lu, P., & Bryant, M. (1996). CHINOOK the world man-machine checkers champion. AI Magazine, 17(1), 21–29. Retrieved from http://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1208

Irving, G., Donkers, J., & Uiterwijk, J. (2000). Solving Kalah. ICGA Journal, 23(3), 139–147. Retrieved from http://www.fdg.unimaas.nl/educ/donkers/pdf/kalah.pdf

Schaeffer, J. (2001). A gamut of games. AI Magazine, 22(3), 29–46. http://doi.org/10.2307/2689640

Bouzy, B., & Cazenave, T. (2001). Computer Go: An AI oriented survey. Artificial Intelligence, 132(1), 39–103. http://doi.org/10.1016/S0004-3702(01)00127-8

Campbell, M., Hoane Jr., a. J., & Hsu, F. (2002). Deep Blue. Artificial Intelligence, 134(1-2), 57–83. http://doi.org/10.1016/S0004-3702(01)00129-1

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

Schiffel, S., & Thielscher, M. (2007). Fluxplayer : A Successful General Game Player. Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-07), 22(2), 1191–1196. Retrieved from https://www.aaai.org/Papers/AAAI/2007/AAAI07-189.pdf

Finnsson, H., & Björnsson, Y. (2008). Simulation-based approach to general game playing. Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008), 259–264. Retrieved from https://www.aaai.org/Library/AAAI/2008/aaai08-041.php

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

Ciancarini, P., & Favini, G. P. (2010). Monte Carlo tree search in Kriegspiel. Artificial Intelligence, 174(11), 670–684. http://doi.org/10.1016/j.artint.2010.04.017

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

Gelly, S., & Silver, D. (2011). Monte-Carlo tree search and rapid action value estimation in computer Go. Artificial Intelligence, 175(11), 1856–1876. http://doi.org/10.1016/j.artint.2011.03.007

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.

Back to Teaching