A growing research community is working towards employing drama management components in story-based games that guide the story towards specific narrative arcs depending on a particular player’s playing patterns. Intuitively, player modeling should be a key component for Drama Manager (DM) based approaches to succeed with human players.
In this paper, we report a particular implementation of the DM component connected to an interactive story game, Anchorhead, while specifically focusing on the player modeling component. We analyze results from our evaluation study and show that similarity in the trace of DM decisions in previous games can be used to predict interestingness of game events for the current player. Results from our current analysis indicate that the average time spent in performing player actions provides a strong distinction between players with varying degrees of gaming experience, thereby helping the DM to adapt its strategy based on this information.
Read the paper:
Evaluating Player Modeling for a Drama Manager Based Interactive Fiction
by Manu Sharma, Manish Mehta, Santi Ontañón, Ashwin Ram
Third Conference on Artificial Intelligence for Interactive Digital Entertainment (AIIDE-07), Workshop on Player Satisfaction, Stanford, CA, June 2007www.cc.gatech.edu/faculty/ashwin/papers/er-07-08.pdf
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Cognitive Computing 