Georgios Yannakakis is Associate Professor at the IT University of Copenhagen

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Reports
  • G. N. Yannakakis, and J. Hallam, "Proceedings of the AIIDE'07 Workshop on Optimizing Player Satisfaction," AAAI Press Technical Report WS-07-01.[pdf]
  • G. N. Yannakakis, and J. Hallam, "Preliminary Studies for Capturing Entertainment through Physiology in Physical Play," Technical Report TR-2007-5, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, June 2007. [pdf]
  • G. N. Yannakakis, and J. Hallam, "Adaptive Approaches for Optimizing Player Satisfaction in Computer and Physical Games: Proceedings of the SAB'06 Workshop," Technical Report TR-2006-2, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, September 2006. [pdf]
  • G. N. Yannakakis, and J. Hallam, "Interesting Games through Stage Complexity and Topology,'' Last Minute results presented in the 8th International Conference on the Simulation of Adaptive Behavior (SAB'04); From Animals to Animats 8, Los Angeles, CA, USA, July 13-17, 2004. [pdf]

Theses

  • G. N. Yannakakis, "AI in Computer Games: Generating Interesting Interactive Opponents by the use of Evolutionary Computation," Ph.D. Thesis, University of Edinburgh, Edinburgh, U.K., October 2005. [pdf]

Abstract: Which features of a computer game contribute to the player’s enjoyment of it? How can we automatically generate interesting and satisfying playing experiences for a given game? These are the two key questions addressed in this dissertation.

Player satisfaction in computer games depends on a variety of factors; here the focus is on the contribution of the behaviour and strategy of game opponents in predator/prey games. A quantitative metric of the ‘interestingness’ of opponent behaviours is defined based on qualitative considerations of what is enjoyable in such games, and a mathematical formulation grounded in observable data is derived. Using this metric, neural-network opponent controllers are evolved for dynamic game environments where limited inter-agent communication is used to drive spatial coordination of opponent teams.

Given the complexity of the predator task, cooperative team behaviours are investigated. Initial candidates are generated using off-line learning procedures operating on minimal neural controllers with the aim of maximising opponent performance. These example controllers are then adapted using on-line (i.e. during play) learning techniques to yield opponents that provide games of high interest. The on-line learning methodology is evaluated using two dissimilar predator/prey games with a number of different computer player strategies. It exhibits generality across the two game test-beds and robustness to changes of player, initial opponent controller selected, and complexity of the game field.

The interest metric is also evaluated by comparison with human judgement of game satisfaction in an experimental survey. A statistically significant number of players were asked to rank game experiences with a test-bed game using perceived interestingness and their ranking was compared with that of the proposed interest metric. The results show that the interest metric is consistent with human judgement of game satisfaction.

Finally, the generality, limitations and potential of the proposed methodology and techniques are discussed, and other factors affecting the player’s satisfaction, such as the player’s own strategy, are briefly considered. Future directions building on the work described herein are presented and discussed.

  • G. N. Yannakakis, "Evolutionary Computation: Research on Emerging Strategies through a Complex Multi-Agent Environment," M.Sc. Thesis, Technical University of Crete, Chania, Greece, September 2001 (in Greek).
  • G. N. Yannakakis, "Artificial Life: An application of Evolutionary Computation,'' Diploma Thesis, Technical University of Crete, Chania, Greece, October 1999 (in Greek).
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Player Satisfaction Modeling
FlatLand
Physical Game Interaction
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Last updated: 27/09/08