publication . Conference object . 2008

A SVM bases AI design for interactive gaming

Jiang, Yang; Jiang, Jianmin; Palmer, Ian;
Open Access English
  • Published: 01 Jan 2008
  • Country: United Kingdom
Abstract
Interactive gaming requires automatic processing on large volume of random data produced by players on spot, such as shooting, football kicking, boxing etc. In this paper, we describe an artificial intelligence approach in processing such random data for interactive gaming by using a one-class support vector machine (OC-SVM). In comparison with existing techniques, our OC-SVM based interactive gaming design has the features of: (i): high speed processing, providing instant response to the players: (i) winner selection and control by one parameter, which can be pre-designed and adjusted according to the game design needs, i.e. level of difficulties: Experiments o...
Subjects
ACM Computing Classification System: ComputingMilieux_PERSONALCOMPUTING
free text keywords: G400
Related Organizations
Download from

[1] Laird J. and Lent M. 'Interactive Computer Games: Human-level AI's Killer Application' National Conference on Artificial Intelligence (AAAI) on August 2, 2000;

[2] J.E. Laird 'Using a Computer Game to Develop Advanced AI', Computer, 34 (7), July 2001, pp. 70-75.

[3] Cho B.H., Jung S.H., Seong Y.R. and Oh H.R. 'Exploiting intelligence in fighting action games using neural networks', IEICE Transactions on Information and Systems, Vol E89D, No 3, 2006, pp1249-1256;

[4] Brian Magerko, John E. Laird, Mazin Assanie, Alex Kerfoot, Devvan Stokes, AI Characters and Directors for Interactive Computer Games, Proceedings of the 2004 Innovative Applications of Artificial Intelligence Conference, San Jose, CA, July 2004. AAAI Press.

[5] Dawes M and Hall R. 'Towards using first-person shooter computer-games as an artificial intelligence testbed, Knowledge based Intelligent Information & Engineering Systems, Proceedings, Vol 3681, 2005, pp276-282;

[6] J. Ma and S. Perkins. “Time-series novelty detection using one-class support vector machines”. In Proceedings of the International Joint Conference on Neural Networks, pp. 1741- 1745, July, 2003.

[7] C. Burges. “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery(2), pp.121-167, 1998.

[8] B. Schölkopf, R. Williamson et al. “Support vector method for novelty detection”, Neural Information processing Systems, MIT Press, pp 582-588, 2000; [OpenAIRE]

[9] Jiang J. 'Parallel and neural network implementation of LBG vector quantization', IEE Proceedings of IPA97: IEE Sixth International Conference on Image Processing, Trinity College, Dublin, Ireland, 1997, ISBN: 0-85296-692-x, ISSN: 0537-9989, pp 27-31.

Any information missing or wrong?Report an Issue