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Invoke Artificial Intelligence and Machine Learning for Strategic-Level Games and Interactive Simulations

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Artificial Intelligence (SLAAI-ICAI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 890))

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Abstract

Computer games are an important sector of the digital economy, computer and entertainment industry are very sophisticated in many ways in the current context of technology. They’ve gone beyond entertainment needs, and the computer game paradigm and technology together are now increasingly used in education, training, storytelling, and wherever it’s necessary to create an appealing and engaging environment. More realism in virtual and artificial environments and more real interfaces to the users can be considered as two main advantages that we get using these techniques. Instead of pre-defined hard coded scripts driving these environments, we will be able to create just the environment and its relative mechanics, so the artificial intelligence could introduce tailored challenges and scenarios to the environment. This paper proposes a Behavioral Driven Procedural Content Generation methodology together with Ternary Neural Networks to be used in interactive strategy-based simulations for effective decision making. This is vital because current approaches like Experience Driven Procedural Content Generation algorithms can be very flexible and one small change could trigger complex changes in the system. Using another model created by using player behavior will be specifying the clamping conditions so the AI is capable of stabilizing itself.

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References

  1. Yannakakis, G.N., Togelius, J.: A panorama of artificial and computational intelligence in games. IEEE Trans. Comput. Intell. AI Games 7(4), 317–335 (2015)

    Article  Google Scholar 

  2. Beck, R., Pauker, S.G.: The Markov process in medical prognosis. Med. Decis. Mak. 3(4), 419–458 (1983)

    Article  Google Scholar 

  3. Lucas, S.M., Mateas, M., Preuss, M., Spronck, P., Togelius, J.: Artificial and computational intelligence in games (Dagstuhl Seminar 12191). In: Dagstuhl Reports, vol. 2, pp. 43–70, no. 5 (2012)

    Google Scholar 

  4. Lucas, S.M., Mateas, M., Preuss, M., Spronck, P., Togelius, J.: Artificial and computational intelligence in games: integration. In: Seminar: 1998 ACM Subject Classification I.2.1 Artificial Intelligence – Games, 25–30 January 2015

    Google Scholar 

  5. Agrawal, A., Gans, J., Goldfarb, A.: What to expect from artificial intelligence. Sloan Management Review (2017)

    Google Scholar 

  6. Brynjolfsson, E., Rock, D., Syverson, C.: Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics. NBER Working Paper No. 24001, November (2017)

    Google Scholar 

  7. Kim, M., Kim, K., Kim, S., Anind, K.D.: Performance evaluation gaps in a real-time strategy game between human and artificial intelligence players. IEEE Access 6, 13575–13586 (2018)

    Article  Google Scholar 

  8. Perez-Liebana, D., Samothrakis, S., Togelius, J., Lucas, S., Schaul, T.: General video game AI: competition, challenges and opportunities. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 4335–4337 (2016)

    Google Scholar 

  9. Baldiris, S., Graf, S., Fabregat, R.: Dynamic user modeling and adaptation based on learning styles for supporting semi-automatic generation of IMS learning design. In: ICALT, 11th IEEE International Conference on Advanced Learning Technologies, 6–8 July 2011, Athens, Georgia, USA

    Google Scholar 

  10. Gardner, H.: Multiple Intelligences: The Theory in Practice. Basic Books, New York (1993)

    Google Scholar 

  11. Hamdaoui, N., Idrissi, M.K., Bennani, S.: AMEG: adaptive mechanism for educational games based on IMSLD and artificial intelligence (2015)

    Google Scholar 

  12. Herik, H.J., Donkers, H.H.L.M., Spronck, P.H.M.: Opponent modelling and commercial games. In: Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG’05) (2005)

    Google Scholar 

  13. Hamdaoui, N., Idrissi, M.K., Bennani, S.: Serious games in education, towards the standardization of the teaching-learning process. In: EMET, 18–20 July 2014

    Google Scholar 

  14. Alemdar, H., Leroy, V., Prost-Boucle, A., Pétrot, F.: Ternary neural networks for resource-efficient AI applications

    Google Scholar 

  15. Bennett, C.C., Hauser, K.: Artificial intelligence framework for simulating clinical decision-making: a markov decision process approach. Artif. Intell. Med. 57, 9–19 (2013)

    Article  Google Scholar 

  16. Yannakakis, G.N., Togelius, J.: Experience-driven procedural content generation. IEEE Trans. Affect. Comput. 2(3), 147–161 (2011)

    Article  Google Scholar 

  17. Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1175–1191 (2001)

    Article  Google Scholar 

  18. Silver, D., et al.: Mastering the game of Go without human knowledge. Nature 550, 354–358 (2007)

    Article  Google Scholar 

  19. Silver, D., et al.: Mastering chess and shogi by self-play with a general reinforcement learning algorithm (2017)

    Google Scholar 

  20. Das, A., Hasan, M.M.: Eye gaze behavior of virtual agent in gaming environment by using artificial intelligence. In: 2013 International Conference on Electrical Information and Communication Technology (EICT) (2013)

    Google Scholar 

  21. Ge, X., Han, Q., Ding, D., Zhang, X., Ning, B.: A survey on recent advances in distributed sampled-data cooperative control of multi-agent systems. Neurocomputing 275, 1684–1701 (2018)

    Article  Google Scholar 

  22. Tu, X., Terzopoulos, D.: Artificial fishes: physics, locomotion, perception, behavior. In: Glassner, A. (ed.) SIGGRAPH 1994: Proceedings of the 21st Annual Conference Series on Computer Graphics and interactive techniques, 24–29 July 1994, Orlando, Florida, pp. 43–50. ACM SIGGRAPH. ACM Press. ISBN 0-89791-667-0

    Google Scholar 

  23. Sud, A., Andersen, E., Curtis, S., Lin, M., Manocha, D.: Real-time path planning for virtual agents in dynamic environments (2013)

    Google Scholar 

  24. Okabe, A., Boots, B., Sugihara, K.: Spatial Tessellations: Concepts and Applications of Voronoi diagrams. Wiley, New York (1992)

    MATH  Google Scholar 

  25. Choset, H., Burdick, J.: Sensor based motion planning: the hierarchical generalized Voronoi graph. In: Algorithms for Robot Motion and Manipulation, pp. 47–61. AK Peters (1996)

    Google Scholar 

  26. Rich, C., Sidner, C.L., Lesh, N.: Applying collaborative discourse theory to human–computer interaction. AI Mag. 22(4), 15–26 (2001)

    Google Scholar 

  27. Stein, J.-P., Ohler, P.: Venturing into the uncanny valley of mind—the influence of mind attribution on the acceptance of human-like characters in a virtual reality setting. Cognition 160, 43–50 (2017). https://doi.org/10.1016/j.cognition.2016.12.010

    Article  Google Scholar 

  28. Waytz, A., Gray, K., Epley, N., Wegner, D.M.: Causes and consequences of mind perception. Trends Cogn. Sci. 14, 383–388 (2010)

    Article  Google Scholar 

  29. Nass, C., Moon, Y.: Machines and mindlessness: social responses to computers. J. Soc. Issues 56(1), 81–103 (2000)

    Article  Google Scholar 

  30. Halsey, L.G., et al.: Acceleration versus heart rate for estimating energy expenditure and speed during locomotion in animals: tests with an easy model species, homo sapiens. Zoology (Jena) 111(3), 231–241 (2008)

    Article  Google Scholar 

  31. Neuman, M.R.H., et al.: Cardiopulmonary monitoring at home: the CHIME monitor. Physiol. Meas. 22(2), 267–286 (2001)

    Article  Google Scholar 

  32. Chuo, Y., et al.: Mechanically flexible wireless multisensor platform for human physical activity and vitals monitoring. IEEE Trans. Biomed. Circuits Syst. 4, 281–294 (2010)

    Article  Google Scholar 

  33. Niazi, M.A., Hussain, A., Kolberg, M.: Verification & validation of agent based simulations using the VOMAS (virtual overlay multi-agent system) approach

    Google Scholar 

  34. Lugmayr, A., Zhu, K., Ma, X.: Artificial intelligence meets virtual and augmented realities

    Google Scholar 

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Acknowledgement

This review paper is built and extends on currently underway and previously published research papers and surveys that were put forth by referenced authors. The author of this review would like to thank the supervisor for her useful comments and immense guidance given in need.

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Authors and Affiliations

  1. Faculty of Information Technology, University of Moratuwa, Katubedda, Sri Lanka

    Nishan Chathuranga Wickramarathna & Gamage Upeksha Ganegoda

Authors
  1. Nishan Chathuranga Wickramarathna
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  2. Gamage Upeksha Ganegoda
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Corresponding author

Correspondence to Gamage Upeksha Ganegoda .

Editor information

Editors and Affiliations

  1. Karunya University, Coimbatore, India

    Jude Hemanth

  2. University of Moratuwa, Moratuwa, Sri Lanka

    Thushari Silva

  3. University of Moratuwa, Moratuwa, Sri Lanka

    Asoka Karunananda

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Wickramarathna, N.C., Ganegoda, G.U. (2019). Invoke Artificial Intelligence and Machine Learning for Strategic-Level Games and Interactive Simulations. In: Hemanth, J., Silva, T., Karunananda, A. (eds) Artificial Intelligence. SLAAI-ICAI 2018. Communications in Computer and Information Science, vol 890. Springer, Singapore. https://doi.org/10.1007/978-981-13-9129-3_10

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  • DOI: https://doi.org/10.1007/978-981-13-9129-3_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9128-6

  • Online ISBN: 978-981-13-9129-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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