Dynamic balancing of game difficulty can help cater for different levels of ability in players. However, performance in some game\r\ntasks depends on not only the player�s ability but also their desire to take risk. Taking or avoiding risk can offer players its own\r\nreward in a game situation. Furthermore, a game designer may want to adjust the mechanics differently for a risky, high ability\r\nplayer, as opposed to a risky, low ability player. In this work, we describe a novel modelling technique known as particle filtering\r\nwhich can be used to model various levels of player ability while also considering the player�s risk profile. We demonstrate this\r\ntechnique by developing a game challenge where players are required to make a decision between a number of possible alternatives\r\nwhere only a single alternative is correct. Risky players respond faster but with more likelihood of failure. Cautious players wait\r\nlonger for more evidence, increasing their likelihood of success, but at the expense of game time. By gathering empirical data for\r\nthe player�s response time and accuracy, we develop particle filter models. These models can then be used in real-time to categorise\r\nplayers into different ability and risk-taking levels.
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