A data mining approach to strategy prediction

CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games

Volumn , Issue , 2009, Pages 140-147

  Weber, Ben G ^a   Mateas, Michael ^a  

^a University of California Santa Cruz   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BUILDING TYPES; DOMAIN KNOWLEDGE; FEATURE VECTORS; GAME LOG; GAME PLAYING; IMPERFECT INFORMATION; LATTICE REPRESENTATION; MACHINE LEARNING ALGORITHMS; MACHINE LEARNING TECHNIQUES; OPPONENT MODELING; PREDICTIVE CAPABILITIES;

ARTIFICIAL INTELLIGENCE; DATA MINING; ENCODING (SYMBOLS); ROBOT LEARNING;

LEARNING ALGORITHMS;

EID: 71549143175     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIG.2009.5286483     Document Type: Conference Paper

References (26)

1. S. Bakkes, P. Spronck, and J. van den Herik, "Rapid Adaptation of Video Game AI," in Proceedings of the IEEE Symposium on Computational Intelligence in Games. Perth, Australia: IEEE, 2008, pp. 79-86.
2. S. Hladky and V. Bulitko, "An Evaluation of Models for Predicting Opponent Positions in First-Person Shooter Video Games," in Proceedings of the IEEE Symposium on Computational Intelligence in Games. Perth, Australia: IEEE, 2008, pp. 39-46.
3. A. Fink, J. Denzinger, and J. Aycock, "Extracting NPC behavior from computer games using computer vision and machine learning techniques," in IEEE Symposium on Computational Intelligence and Games. Honolulu, Hawaii: IEEE, 2007, pp. 24-31.
4. M. Fagan and P. Cunningham, "Case-based plan recognition in computer games," Lecture notes in computer science, vol. 2689, pp. 161-170, 2003.
5. E. Charniak and R. P. Goldman, "A bayesian model of plan recognition," Artificial Intelligence, vol. 64, no. 1, pp. 53-79, 1993.
6. D. Albrecht, I. Zukerman, and A. Nicholson, "Bayesian Models for Keyhole Plan Recognition in an Adventure Game," User Modeling and User-Adapted Interaction, vol. 8, no. 1, pp. 5-47, 1998.
7. S. Carberry, "Techniques for plan recognition," User Modeling and User-Adapted Interaction, vol. 11, no. 1-2, pp. 31-48, 2001.
8. D. Cheng and R. Thawonmas, "Case-based plan recognition for realtime strategy games," in Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education (CGAIDE 2004). Reading, UK: University of Wolverhampton, 2004, pp. 36-40.
9. M. Molineaux, D.W. Aha, and P. Moore, "Learning Continuous Action Models in a Real-Time Strategy Environment," in Proceedings of the Florida Artificial Intelligence Research Conference. Coconut Grove, Florida: AAAI Press, 2008, pp. 257-262.
10. R.-K. Balla and A. Fern, "UCT for Tactical Assault Planning in Real-Time Strategy Games," in Proceedings of the International Joint Conference on Artificial Intelligence. Pasadena, California: Morgan Kaufmann, 2009, pp. 40-45.
11. M. J. V. Ponsen, H. Muñoz-Avila, P. Spronck, and D. W. Aha, "Automatically Acquiring Domain Knowledge For Adaptive Game AI Using Evolutionary Learning," in Proceedings of the AAAI Conference on Artificial Intelligence. Pittsburgh, Pennsylvania: AAAI Press, 2005, pp. 1535-1540.
12. D. Aha, M. Molineaux, and M. Ponsen, "Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game," Lecture notes in computer science, vol. 3620, pp. 5-20, 2005.
13. M. Ponsen, "Improving adaptive game AI with evolutionary learning," Master's thesis, Delft University of Technology, Delft, the Netherlands, 2004.
14. S. Ontañón, K. Mishra, N. Sugandh, and A. Ram, "Learning from Demonstration and Case-Based Planning for Real-Time Strategy Games," Soft Computing Applications in Industry, pp. 293-310, 2008.
15. K. Mishra, S. Ontañón, and A. Ram, "Situation assessment for plan retrieval in real-time strategy games," in Proceedings of the European Conference on Case-Based Reasoning. Trier, Germany: Springer, 2008, pp. 355-369.
16. J. Hsieh and C. Sun, "Building a player strategy model by analyzing replays of real-time strategy games," in Proceedings of the International Joint Conference on Neural Networks. Hong Kong, China: IEEE, 2008, pp. 3106-3111.
17. B. G. Weber and M. Mateas, "Conceptual Neighborhoods for Retrieval in Case-Based Reasoning," in Proceedings of the International Conference on Case-Based Reasoning. Seattle, Washington: Springer, 2009, pp. 343-357.
18. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, California: Morgan Kaufmann, 1993.
19. D. W. Aha, D. Kibler, and M. K. Albert, "Instance-based learning algorithms," Machine Learning, vol. 6, no. 1, pp. 37-66, 1991.
20. B. Martin, "Instance-based learning: nearest neighbour with generalisation," Master's thesis, University of Waikato, Hamilton, New Zealand, 1995.
21. J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression: A Statistical View of Boosting," Annals of Statistics, vol. 28, no. 2, pp. 337-374, 2000.
22. I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques. San Francisco, California: Morgan Kaufmann, 2005.
23. H. Akaike, "A new look at the statistical model identification, " IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716-723, 1974.
24. Y. Wang and I. Witten, "Inducing model trees for continuous classes," in Poster Papers of the European Conference on Machine Learning, Prague, Czech Republic, 1997, pp. 128-137.
25. J. Friedman, "Stochastic gradient boosting," Computational Statistics and Data Analysis, vol. 38, no. 4, pp. 367-378, 2002.
26. J. McCoy and M. Mateas, "An Integrated Agent for Playing Real-Time Strategy Games," in Proceedings of the AAAI Conference on Artificial Intelligence. Chicago, Illinois: AAAI Press, 2008, pp. 1313-1318.