Challenge-Sensitive Action Selection: an Application to Game Balancing
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
The case for dynamic difficulty adjustment in games
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
Player Modeling for Intelligent Difficulty Adjustment
DS '09 Proceedings of the 12th International Conference on Discovery Science
Towards capturing and enhancing entertainment in computer games
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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Dynamic balancing of game difficulty can help cater for different levels of ability in players. However, performance in some game tasks depends on not only the player's ability but also their desire to take risk. Taking or avoiding risk can offer players its own reward in a game situation. Furthermore, a game designer may want to adjust the mechanics differently for a risky, high ability player, as opposed to a risky, low ability player. In this work, we describe a novel modelling technique known as particle filtering which can be used to model various levels of player ability while also considering the player's risk profile. We demonstrate this technique by developing a game challenge where players are required to make a decision between a number of possible alternatives where only a single alternative is correct. Risky players respond faster but with more likelihood of failure. Cautious players wait longer for more evidence, increasing their likelihood of success, but at the expense of game time. By gathering empirical data for the player's response time and accuracy, we develop particle filter models. These models can then be used in real-time to categorise players into different ability and risk-taking levels.