Abstract
The complexity of training situations requires teaching different skills to different trainees and in different situations. Current approaches of dynamic difficulty adjustment in games use a purely centralized approach for this adaptation. This becomes impractical if the complexity increases and especially if past actions of the non player characters need to be taken into account. Agents are increasingly used in serious game implementations as a means to reduce complexity and increase believability. Agents can be designed to adapt their behavior to different user requirements and situations. However, this leads to situations in which the lack of coordination between the agents makes it practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee.
In this paper, we present a monitoring system for the coordination of the characters actions and adaptation to guarantee appropriate combinations of character actions that ensure the preservation of the storyline. In particular we propose an architecture for game design that introduces a monitoring module to check the development of user skills and direct coordinated agent adaptation. That is, agents propose possible courses of action that are fitting their role and context, and the monitor module uses this information together with its evaluation of user level and storyline progress to determine the most suitable combination of proposals.
Categories and Subject Descriptors:
I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence
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Westra, J., Dignum, F., Dignum, V. (2011). Guiding User Adaptation in Serious Games. In: Dignum, F. (eds) Agents for Games and Simulations II. AGS 2010. Lecture Notes in Computer Science(), vol 6525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18181-8_9
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