User Tools

Site Tools


teaching:2016summer:bogaalgos

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.

Back to Teaching

teaching/2016summer/bogaalgos.txt · Last modified: 2016/06/22 13:52 by Pedro Feijao