- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Advances in Artificial Intelligence
Volume 2013 (2013), Article ID 357068, 9 pages
Tree Pruning for New Search Techniques in Computer Games
Distributed Computing Systems, Belfast, UK
Received 28 May 2012; Revised 10 October 2012; Accepted 24 October 2012
Academic Editor: Srinivas Bangalore
Copyright © 2013 Kieran Greer. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- J. Schaeffer, “The history heuristic and alpha-beta search enhancements in practice,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 11, pp. 1203–1212, 1989.
- J. Schaeffer and A. Plaat, “New advances in alpha-beta searching,” in Proceedings of the 25th ACM Computer Science Conference, pp. 124–130, February 1996.
- K. Greer, “Computer chess move-ordering schemes using move influence,” Artificial Intelligence, vol. 120, no. 2, pp. 235–250, 2000.
- A. Fernández and A. Salmerón, “BayesChess: a computer chess program based on Bayesian networks,” Pattern Recognition Letters, vol. 29, no. 8, pp. 1154–1159, 2008.
- A. Iqbal, “What computer chess still has to teach us—the game that will not go,” Electronic Journal of Computer Science and Information Technology, vol. 2, no. 1, pp. 23–29, 2010.
- J. Fürnkranz, “Recent advances in machine learning and game playing,” Oesterreichische Gesellschaft fuer Artificial Intelligence Journal, vol. 26, no. 2, pp. 19–28, 2007.
- J. Baxter, A. Tridgell, and L. Weaver, “KnightCap: a chess program that learns by combining TD(lambda) with gametree search,” in Proceedings of the 15th International Conference on Machine Learning, pp. 28–36, 1998.
- S. Thrun, “Learning to play the game of chess,” in Advances in Neural Information Processing Systems 7, G. Tesauro, D. Touretzky, and T. Leen, Eds., Morgen Kaufmann, San Fransisco, Calif, USA, 1995.
- D. B. Fogel, T. J. Hays, S. L. Hahn, and J. Quon, “A self-learning evolutionary chess program,” Proceedings of the IEEE, vol. 92, no. 12, pp. 1947–1954, 2004.
- B. Bonet and H. Geffner, “Learning depth-first search: a unified approach to heuristic search in deterministic and non-deterministic settings, and its application to MDPs,” in Proceedings of the 16th International Conference on Automated Planning and Scheduling (ICAPS'06), D. Long, S. F. Smith, D. Borrajo, and L. Mc-Cluskey, Eds., pp. 142–151, June 2006.
- S. Gelly and D. Silver, “Combining online and offline knowledge in UCT,” in Proceedings of the 24th International Conference on Machine Learning (ICML'07), pp. 273–280, Corvallis, Ore, USA, June 2007.
- Y. Wang and S. Gelly, “Modifications of UCT and sequence-like simulations for Monte-Carlo Go,” in Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG'07), pp. 175–182, Honolulu, Hawaii, USA, April 2007.
- J. Fürnkranz, “Machine learning in computer chess: the next generation,” The International Computer-Chess Association Journal, vol. 19, no. 3, pp. 147–161, 1995.