AI and Games
Machine Learning Analysis of Player Behaviour in Tomb Raider: Underworld | AI and Games (2018x12)
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A written version of this video is available:
The AI and Games website:
http://aiandgames.com/tomb-raider/
Medium:
https://medium.com/@t2thompson/tombraider-60682f8fe36f
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Tomb Raider: Underworld may well be over a decade old, but it's home to a number of exciting research projects that sought to understand how players play the game once it is released to market.
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Research papers referenced in this episode are listed below:
- Drachen, A., & Canossa, A. (2009). Analyzing spatial user behavior in computer games using geographic information systems. In Proceedings of the 13th international MindTrek conference: Everyday life in the ubiquitous era (pp. 182-189). ACM.
https://www.researchgate.net/profile/Alessandro_Canossa/publication/307476789_Analyzing_User_Behavior_in_Digital_Games/links/5a1ee55b0f7e9b9d5e005056/Analyzing-User-Behavior-in-Digital-Games.pdf
- Drachen, A., Canossa, A., & Yannakakis, G. N. (2009). Player modeling using self-organization in Tomb Raider: Underworld. In Computational Intelligence and Games, 2009. CIG 2009. IEEE Symposium on (pp. 1-8). IEEE.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.456.4596&rep=rep1&type=pdf
- Mahlmann, T., Drachen, A., Togelius, J., Canossa, A., & Yannakakis, G. N. (2010). Predicting player behavior in Tomb Raider: Underworld. In Computational Intelligence and Games (CIG), 2010 IEEE Symposium on (pp. 178-185). IEEE.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.180.6405&rep=rep1&type=pdf
- Sifa, R., Drachen, A.,