Skip to main content

Advertisement

Springer Nature Link
Log in

Meme representations for game agents

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

The advancement in game technology has served to enrich player’s gaming experience in a substantial way. Nowadays, it is common to have blockbuster quality games, with realistic graphics and engaging stories. Despite this, the progress made in incorporating Artificial Intelligence has been slow, and realistic human-like intelligence in games is hardly to be found. There have been some attempts to use Machine Learning in games, but such attempts often ended up impractical or affecting the players enjoyment due to several constraining factors. In this paper, we describe Meme War as a proof-of-concept for practical usage of Machine Learning in games. We introduce Extreme Learning Machine (ELM) as one approach to achieve a better experience in employing Machine Learning in games. Advantages of ELM over Multilayer Perceptron (MLP) are presented in terms of what ELM can offer in a practical point of view of the player.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Blackmore, S.: The Meme Machine. Oxford University Press, New York (1999)

    Google Scholar 

  2. Brockington, M., Darrah, M.: How not to implement a basic scripting language. In: Rabin, S. (ed.) AI Game Programming Wisdom, chap. 10, pp. 548–554. Charles River Media (2002)

  3. Cao, Q., Lim, M.H., Li, J.H., Ong, Y.S., Ng, W.L.: A context switchable fuzzy inference chip. IEEE Trans. Fuzzy Syst. 14(4), 552–567 (2006)

    Article  Google Scholar 

  4. Chen, X., Ong, Y.S.: A conceptual modeling of meme complexes in stochastic search. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(5), 612–625 (2012)

    Article  Google Scholar 

  5. Chen, X., Ong, Y.S., Lim, M.H., Tan, K.C.: A multi-facet survey on memetic computation. IEEE Trans. Evolution. Comput. 15(5), 591–607 (2011)

    Article  Google Scholar 

  6. Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Contr. Signals Syst. (MCSS) 2(4), 303–314 (1989). doi:10.1007/BF02551274

    Article  MATH  MathSciNet  Google Scholar 

  7. Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (1976)

    Google Scholar 

  8. Demasi, P., de O. Cruz, A.J.: Online coevolution for action games. Int. J. Intell. Games Simul. 2(2), 80–88 (2003)

    Google Scholar 

  9. Feng, G., Huang, G.B., Lin, Q., Gay, R.: Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans. Neural Netw. 20(8), 1352–1357 (2009). doi:10.1109/TNN.2009.2024147

    Article  Google Scholar 

  10. Grant, E.F., Lardner, R.: The talk of the town - It. http://www.newyorker.com/archive/1952/08/02/1952_08_02_018_TNY_CARDS_000236053 (1952). [Online] Accessed 23 May 2012

  11. Grants, G.: Memetic Lexicon (1990). http://www.lucifer.com/virus/memlex.html. Accessed 23 May 2012

  12. Huang, G.B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70, 3056–3062 (2007). doi:10.1016/j.neucom.2007.02.009

    Article  Google Scholar 

  13. Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879–892 (2006). doi:10.1109/TNN.2006.875977

    Article  Google Scholar 

  14. Huang, G.B., Wang, D., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2, 107–122 (2011)

    Article  Google Scholar 

  15. Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 513–529 (2012). doi:10.1109/TSMCB.2011.2168604

    Article  Google Scholar 

  16. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)

    Article  Google Scholar 

  17. Kan, E., Lim, M., Ong, Y., Tan, A., Yeo, S.: Extreme learning machine terrain-based navigation for unmanned aerial vehicles. Neural Comput. Appl. 22(3–4), 469–477 (2013). doi:10.1007/s00521-012-0866-9

    Article  Google Scholar 

  18. Laird, J.E., van Lent, M.: Human-level AI’s killer application: interactive computer games. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, pp. 1171–1178. AAAI Press / The MIT Press (2000). http://citeseer.ist.psu.edu/laird00humanlevel.html

  19. Lynch, A.: Thought contagion as abstract evolution. J. Ideas 2, 3–10 (1991)

    Google Scholar 

  20. Malathi, V., Marimuthu, N., Baskar, S.: Intelligent approaches using support vector machine and extreme learning machine for transmission line protection. Neurocomputing 73(10–12), 2160–2167 (2010). doi:10.1016/j.neucom.2010.02.001. http://www.sciencedirect.com/science/article/pii/S0925231210000822

    Article  Google Scholar 

  21. Meuth, R., Lim, M.H., Ong, Y.S., Wunsch II, D.C.: A proposition on memes and meta-memes in computing for higher-order learning. Memetic Comp. 1(2), 85–100 (2009)

    Article  Google Scholar 

  22. Miche, Y., van Heeswijk, M., Bas, P., Simula, O., Lendasse, A.: TROP-ELM: a double-regularized ELM using LARS and tikhonov regularization. Neurocomputing 74(16), 2413–2421 (2011). doi:10.1016/j.neucom.2010.12.042

    Article  Google Scholar 

  23. Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., Lendasse, A.: OP-ELM: optimally-pruned extreme learning machine. IEEE Trans. Neural Netw. 21(1), 158–162 (2010). doi:10.1109/TNN.2009.2036259

    Article  Google Scholar 

  24. Miles, C., Louis, S.J., Cole, N., McDonnell, J.: Learning to play like a human: case injected genetic algorithms applied to strategic computer game playing. In: Proceedings of the IEEE Congress on Evolutionary Computation - CEC (2004)

  25. Minhas, R., Baradarani, A., Seifzadeh, S., Wu, Q.J.: Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing 73(10–12), 1906–1917 (2010). doi:10.1016/j.neucom.2010.01.020. http://www.sciencedirect.com/science/article/pii/S0925231210001517

    Article  Google Scholar 

  26. Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. B, Cybern. (USA) 36(1), 141–152 (2006)

    Article  Google Scholar 

  27. Ontañón, S., Mishra, K., Sugandh, N., Ram, A.: Case-based planning and execution for real-time strategy games. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 164–178. Springer, Heidelberg (2007)

    Google Scholar 

  28. Priesterjahn, S., Kramer, O., Weimer, A., Goebels, A.: Evolution of human-competitive agents in modern computer games. In: IEEE Congress on Evolutionary Computation - CEC, pp. 777–784 (2006)

  29. Rao, C.R., Mitra, S.K.: Generalized Inverse of Matrices and Its Applications. John Wiley & Sons Inc, New York (1972)

    Google Scholar 

  30. Rosenblatt, F.: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC (1962)

    MATH  Google Scholar 

  31. Schaeffer, J.: A gamut of games. AI Magaz. 22(3), 29–46 (2001)

    Google Scholar 

  32. Scott, B.: The illusion of intelligence. In: Rabin, S. (ed.) AI Game Programming Wisdom, chap. 1, pp. 16–20. Charles River Media (2002)

  33. Singapore-mit game lab gambit. http://gambit.mit.edu/. Accessed 23 May 2012

  34. Snider, M.: Video game sales have disappointing december and annual drop. (2012) http://content.usatoday.com/communities/gamehunters/post/2012/01/video-game-sales-have-disappointing-december-and-annual-drop/1#.T-MHCG-76Go

  35. Spronck, P., Sprinkhuizen-Kuyper, I., Postma, E.: Online adaptation of game opponent ai in simulation and in practice. In: Mehdi, Q., Gough, N., Natkin, S. (eds.) Proceedings of the 4th International Conference on Intelligent Games and Simulation, pp. 93–100 (2003)

  36. Stanley, K., Bryant, B., Karpov, I., Miikkulainen, R.: Real-time evolution of neural networks in the nero video game. In: Proceedings Twenty-First National Conference on Artificial Intelligence (AAAI-06). Eighteenth Innovative Applications of Artificial Intelligence Conference (IAAI-06), vol. 2, pp. 1671–1674 (2006)

  37. Tozour, P.: The evolution of game AI. In: Rabin, S. (ed.) AI Game Programming Wisdom, chap. 1, pp. 3–15. Charles River Media (2002)

  38. Tozour, P.: The perils of ai scripting. In: Rabin, S. (ed.) AI Game Programming Wisdom, chap. 10, pp. 541–547. Charles River Media (2002)

  39. van Heeswijk, M., Miche, Y., Oja, E., Lendasse, A.: GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing 74(16), 2430–2437 (2011). doi:10.1016/j.neucom.2010.11.034

    Article  Google Scholar 

  40. Wang, G., Zhao, Y., Zhao, X., Wang, B., Qiao, B.: Efficiently mining local conserved clusters from gene expression data. Neurocomputing 73(7–9), 1425–1437 (2010). doi:10.1016/j.neucom.2009.11.009

    Article  Google Scholar 

  41. Woodcock, S.: Game AI: The State of the Industry. Game Developer Magazine (2002)

  42. Yu, Q., Miche, Y., Eirola, E., van Heeswijk, M., Séverin, E., Lendasse, A.: Regularized extreme learning machine for regression with missing data. Neurocomputing 102, 45–51 (2013). doi:10.1016/j.neucom.2012.02.040

    Article  Google Scholar 

  43. Zhao, X., Wang, G., Bi, X., Gong, P., Zhao, Y.: Xml document classification based on elm. Neurocomputing 74(16), 2444–2451 (2011). doi:10.1016/j.neucom.2010.12.038. http://www.sciencedirect.com/science/article/pii/S0925231211002529

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore

    Yoan Miche & Meng-Hiot Lim

  2. School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore

    Yew-Soon Ong

  3. Department of Information and Computer Science, Aalto University School of Science, FI-00076, Aalto, Finland

    Yoan Miche & Amaury Lendasse

  4. IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain

    Amaury Lendasse

  5. Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, Paseo Manuel Lardizabal 1, Donostia/San Sebastian, Spain

    Amaury Lendasse

Authors
  1. Yoan Miche
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Meng-Hiot Lim
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Amaury Lendasse
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yew-Soon Ong
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yoan Miche.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Miche, Y., Lim, MH., Lendasse, A. et al. Meme representations for game agents. World Wide Web 18, 215–234 (2015). https://doi.org/10.1007/s11280-013-0219-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-013-0219-3

Keywords