Abstract
Evaluating the behaviour of non-player characters is a complex problem. The multifaceted goals of non-player characters include:
• believable, realistic or intelligent behaviour;
• support for game flow;
• player engagement and satisfaction.
This list suggests at least three areas for comparing the behaviour of non-player characters: in terms of player satisfaction, in terms of game flow and in terms of the believability or intelligence of the character’s behaviour.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
G.N. Yannakakis and J. Hallam, Towards Capturing and Enhancing Entertainment in Computer Games, The Fourth Hellenic Conference on Artificial Intelligence, Springer-Verlag, pp. 432–442, 2006.
P. Rani, N. Sarkar and C. Liu, Maintaining optimal challenge in computer games through real-time physiological feedback, HCI International, Las Vegas, USA, 2005.
M. Csikszentmihalyi, Flow: the psychology of optimal experience, Harper Perennial, 1990.
J. Chen, Flow in games (and everything else). Communications of the ACM 50(4):31–34, 2007.
J. Chen, Flow in Games. http://www.jenovachen.com/flowingames/about.htm (Accessed July, 2008).
M. Duggan, The official guide to 3D game studio, Thomson Course Technology, 2007.
E. Byrne, Game level design, Cengage Delmar Learning, 2004.
A. Doull, The death of the level designer: procedural content generation in games. http://roguelikedeveloper.blogspot.com/2008/01/death-of-level-designer-procedural.html (Accessed July, 2008).
L. Kaelbling, M. Littman and A. Moore, Reinforcement learning: a survey. Journal of Artificial Intelligence Research 4:237–285, 1996.
D. Berry and B. Fristedt, Bandit problems: sequential allocation of experiments, Chapman and Hall, London, 1985.
R.S. Sutton and A.G. Barto, Reinforcement learning: an introduction, The MIT Press Cambridge, Massachusetts, London, England, 2000.
B. Hengst, Discovering hierarchy in reinforcement learning with HEXQ, The 19th International Conference on Machine Learning, University of New South Wales, Sydney, Australia, pp. 243–250, 2002.
S. Singh, A.G. Barto and N. Chentanez, Intrinsically motivated reinforcement learning, Advances in Neural Information Processing Systems (NIPS), 17:1281–1288, 2005.
J. Schmidhuber, Exploring the predictable. In A. Ghosh, S. Tsutsui (Eds.), Advances in Evolutionary Computing, pp. 579-612, Springer, 2002.
F. Kaplan and P.-Y. Oudeyer, Motivational principles for visual know-how development. In: C.G. Prince, L. Berthouze, H. Kozima, D. Bullock, G. Stojanov and C. Balkenius (Eds.), Proceedings of the 3rd international workshop on Epigenetic Robotics: Modelling cognitive development in robotic systems, Lund University Cognitive Studies, pp. 73–80, 2003.
X. Huang and J. Weng, Inherent value systems for autonomous mental development, International Journal of Humanoid Robotics, 4(2):407–433, 2007.
R. Saunders and J.S. Gero, Designing for interest and novelty: motivating design agents, CAAD Futures 2001, Kluwer, Dordrecht, pp. 725–738, 2001.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Merrick, K.E., Maher, M.L. (2009). Comparing the Behaviour of Learning Agents. In: Motivated Reinforcement Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89187-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-89187-1_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89186-4
Online ISBN: 978-3-540-89187-1
eBook Packages: Computer ScienceComputer Science (R0)