- 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
- 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]
- 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
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).