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Space Maze: Experience-driven game camera control

Virtual camera control is a key factor in game experience because the camera dictates how players see the game world. As the complexity and unpredictability of games increases, automatic camera control becomes a fundamental requirement. In this paper, we present a game technology demonstrator that showcases automatic camera control capable of creating dissimilar experiences within a 3D prey/predator game. An adaptation algorithm informed by predictors of subjective experiences adjusts the behavior of the camera to influence the experience of the player throughout the game. 1. ADAPTIVE CAMERA CONTROL Camera control is an important component of player experience [4]: the camera viewpoint defines the amount of information shown to the player and thus has a direct impact on the perceived challenge [5] as well as other aspects of the experience, such as player frustration [6]. While most research on automatic camera control techniques has centered around the efficient placement of the camera, determination of the viewpoint and the ease of use (e.g. [3]), a number of studies have investigated the connections between camera control and player experience. Burelli and Yannakakis [2] studied automatic camera control in relation to playing and gaze behavior, building computational models of camera view preferences based on the players’ behavior. Yannakakis et al. [6] investigated a larger variety of experiences focusing on the relation between camera and several affective states, such as frustration and excitement. Computational models mapping the player’s physiological state and the game’s camera profile to subjective self-reports of experience were built facilitating an objective estimator of the player’s affective state in relation to camera behavior. Similar models — substituting physiological information with gameplay data — were used to implement a demonstrator of affective camera control in which http://www.aigameresearch.org/demo-item/mazeball/ the camera controller relied on the prediction of the models driving the experience towards the target affective states. The demonstrator presented in this paper utilizes similar models but introduces camera adaptation as an active game mechanic rather than a subtle change in the background. We expect that as a consequence, the effect of camera adaptation will be clearly manifested in the players’ behavior providing a validation for experience-driven camera control. In the following sections the game mechanics, model construction and adaptation scheme are described in detail.