RTS game strategy evaluation using extreme learning machine

Soft Computing

Volumn 16, Issue 9, 2012, Pages 1627-1637

  Li, Yingjie ^a   Li, Yan ^b   Zhai, Junhai ^b   Shiu, Simon ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b HEBEI UNIVERSITY   (China)

Author keywords

Extreme learning machine; Feature interaction; Real time strategy (RTS) game; Warcraft

Indexed keywords

COMPLEX INTERACTION; DECISION MODELS; EXTREME LEARNING MACHINE; FEATURE INTERACTIONS; FUZZY MEASURES; GAME RULES; GAME STRATEGIES; MILITARY UNITS; PRODUCTION SEQUENCES; REAL TIME STRATEGIES; REAL-TIME STRATEGY GAMES; TREE-BASED; UNIT GROUP; WARCRAFT; WEIGHTED AVERAGE MODELS;

LEARNING SYSTEMS; PRODUCTION ENGINEERING; REAL TIME SYSTEMS;

COMPUTER SOFTWARE;

EID: 84866239975     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-012-0831-7     Document Type: Article

References (46)

1. Aha D, Molineaux M, Ponsen M (2005) Learning to win: Case-based plan selection in a real-time strategy game. In: Case-based reasoning research and development, pp 5-20.
2. Chacko BP, Vimal Krishnan VR, Raju G, Babu Anto P (2011) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cybern doi: 10. 1007/s13042-011-0049-5.
3. Chen C-Y, Zhang J-P, He X-F, Zhou Z-H (2011) Non-parametric kernel learning with robust pairwise constraints. Int J Mach Learn Cybern. doi: 10. 1007/s13042-011-0048-6.
4. Choquet (1953) Theory of capacities. Annales de l'Institut Fourier 5: 131-295.
5. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3): 273-297.
6. Dodge Y (2003) The Oxford dictionary of statistical terms. Oxford University Press, Oxford. ISBN: 0-19-920613-9.
7. Feng G, Huang G-B, Lin Q, Gay R (2009) Error minimized extreme learning machine with growth of hidden nodes and incremental learning. Neural Netw 20: 1352-1357.
8. Frenay B, Verleysen M (2010) Using svms with randomised feature spaces: an extreme learning approach. In: Proceedings of the 18th European symposium on artificial neural networks (ESANN), (Bruges, Belgium), 28-30 April 2010, pp 315-320.
9. Hagelback J, Johansson S (2008) Using multi-agent potential fields in real-time strategy games. In: Proceedings of the 7th international joint conference on autonomous agents and multiagent systems, vol 2, pp 631-638.
10. Hagelback J, Johansson S (2008) Demonstration of multi-agent potential fields in real-time strategy games. In: International foundation for autonomous agents and multiagent systems, Richland, SC.
11. Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1): 55-67.
12. Hsieh JL, Sun CT (2008) Building a player strategy model by analyzing replays of real-time strategy games. In: Proceedings of IEEE international joint conference on neural networks, Hongkong, China, June 2008, pp 3106-3111.
13. Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70: 3056-3062.
14. Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71: 3460-3468.
15. Huang G-B, Zhu QY, Siew CK (2006a) Extreme learning machine: theory and applications. Neurocomputing 70(1-3): 489-501.
16. Huang G-B, Chen L, Siew C-K (2006b) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4): 879-892.
17. Huang G-B, Li M-B, Chen L, Siew C-K (2008) Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing 71: 576-583.
18. Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74: 155-163.
19. Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2): 107-122.
20. Ishii K, Sugeno M (1985) A model of human evaluation process using fuzzy measure. Int J Man Mach Stud 22: 19-38.
21. Jang S-H, Cho S-B (2008) Evolving neural NPCs with layered influence map in the real-time simulation game 'Conqueror'. In: IEEE symposium on computational intelligence and games. pp 385-388.
22. Jun W, Shitong W, Chung F-l (2011) Positive and negative fuzzy rule system, extreme learning machine and image classification. Int J M Learn Cybern 2(4): 261-271.
23. Ksantini R, Boufama B (2011) Combining partially global and local characteristics for improved classification. Int J Mach Learn Cybern. doi: 10. 1007/s13042-011-0045-9.
24. Kwon S, Sugeno M (2000) A hierarchical subjective evaluation model using non-monotonic fuzzy measures and the Choquet integral. In: Fuzzy measures and integrals-theory and applications. Studies in fuzziness and soft computing, Physica, New York, pp 375-391.
25. Li M-B, Huang G-B, Saratchandran P, Sundararajan N (2005) Fully complex extreme learning machine. Neurocomputing 68: 306-314.
26. Li YJ, Peter H, Ng F, Wang HB, Li Y, Simon C, Shiu K (2010) Applying fuzzy integral for performance evaluation in real time strategy game. In: Proceedings of 2010 2nd international conference on information and multimedia technology (ICIMT 2010), Hong Kong, China, December 28-30, vol 1, pp 168-172.
27. Li YJ, Peter H, Ng F, Wang HB, Shiu SCK, Li Y (2011) Apply different fuzzy integrals in unit selection problem of real time strategy game. In: Proceedings of 2011 international conference on fuzzy system, Taiwan, China, June 27-30.
28. Liang N-Y, Huang G-B, Saratchandran P, Sundararajan N (2006) A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6): 1411-1423.
29. Liu Q, He Q, Shi Z (2008) Extreme support vector machine classifier. Lect Notes Comput Sci 5012: 222-233.
30. Miller R (1997) Beyond ANOVA: basics of applied statistics. Chapman & Hall/CRC, Boca Raton.
31. Murofushi Michio T, Machida M (1994) Non-monotonic fuzzy measures and the Choquet integral. Fuzzy Sets Syst 64(1): 73-86.
32. Murofushi T, Sugeno M (2000a) The Choquet integral in multiattribute decision making. In: Fuzzy measures and integrals: theory and applications. Studies in fuzziness and soft computing, Physica, New York, pp 333-347.
33. Murofushi T, Sugeno M (2000b) Fuzzy measures and fuzzy integrals. In: Fuzzy measures and integrals: theory and applications. Studies in fuzziness and soft computing, Physica, New York, pp 3-41.
34. Niu B, Wang H, Peter H, Ng F, Simon C, Shiu K (2009) A neural-evolutionary model of case-based planning in real time strategy games. In: Lecture notes in computer science, vol 5579/2009, pp 291-300. doi: 10. 1007/978-3-642-02568-6_30.
35. Ontanon S, Mishra K, Ram A (2008) Learning from demonstration and case-based planning for real-time strategy games. In: Studies in fuzziness and soft computing, vol 226, pp 293-310. doi: 10. 1007/978-3-540-77465-5_15.
36. Peter H, Ng F, Simon C, Shiu K, Haibo Wang (2009) Learning player behaviors in real time strategy games from real data. In: Twelve rough sets, fuzzy sets, data mining, and granular-soft computing (RSFDGrC), pp 321-327.
37. Peters J, Han L, Ramanna S (1999) The Choquet integral in a rough software cost decision system. Fuzzy measures and integrals: theory and applications. Studies in fuzziness and soft computing, Physica, New York, pp 392-414.
38. Sharma M, Holmes M, Santamaria J, Irani A, Isbell C, Ram A (2007) Transfer learning in real-time strategy games using hybrid CBR/RL. In: Proceedings of the twentieth international joint conference on artificial intelligence.
39. Wang X-Z, Dong C-R (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3): 556-567.
40. Wang J, Wang Z (1997) Using neural networks to determine Sugeno measures by statistics. Neural Netw 10(1): 183-195.
41. Wang Z, Leung KS, Wang J (1997) Genetic algorithms used for determining nonadditive set functions in information fusion. Proc IFSA'97 1: 518-521.
42. Wang ZY, Xu K, Wang J (1999) Using genetic algorithm to determine non-negative monotone set functions for information fusion in environments with random perturbation. Int J Intell Syst 14: 949-962.
43. Wang X, Shao Q, Miao Q, Zhai J (2011a) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing (submitted).
44. Wang X-Z, Chen A-X, Feng H-M (2011b) Upper integral network with extreme learning mechanism. Neurocomputing 74(16): 2520-2525. http://dx. doi. org/10. 1016/j. neucom. 2010. 12. 034.
45. Wang X-Z, Dong L-C, Yan J-H (2011c) Maximum ambiguity based sample selection in fuzzy decision tree induction. IEEE Trans Knowl Data Eng. doi: 10. 1109/TKDE. 2011. 67.
46. Yeung D, Wang X, Tsang E (2004) Handling interaction in fuzzy production rule reasoning. IEEE Trans Syst Man Cybern Part B Cybern 34: 1979-1987. doi: 10. 1109/TSMCB. 2004. 831460.