Modeling Users' Emotions During Interactive Entertainment Sessions

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ABSTRACT We use the formalism of decision theory to develop principled definitions of emotional states of a user, and we postulate that these notions should be useful in designing interactive entertainment systems.

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    ABSTRACT: We describe preliminary research on devising intelligent agents that can improve the educational effectiveness of collaborative, educational computer games. We illustrate how these agents can overcome some of the shortcomings of educational games by explicitly monitoring how students interact with the games, by modeling both the students' cognitive and emotional states, and by generating calibrated interventions to trigger constructive reasoning and reflection when needed. Introduction In this paper, we explore the potential of enriching educational computer games with socially intelligent agents that can help students learn effectively from the games while maintaining the high level of engagement and motivation that constitutes the strong appeal of electronic games in non-educational settings. Our research is developed in the context of EGEMS, the Electronic Games for Education in Math and Science project at the University of British Columbia (UBC). EGEMS is an interdisciplinary pro...
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    ABSTRACT: Computer and videogames have been described using several formal systems – in this paper we consider them as Information Systems. In particular, we use a Decision Theoretic approach to model players dynamically in realtime Pacman (Namco 1980). The method described provides low-level in-game data capture which can provide a simple metric of challenge and player skill, which are key components in measuring the optimality of player experience based on Flow theory. Our approach is based on the calculation of optimal choices available to a player based on key utilities for a given game state. Our hypothesis is that observing a player’s deviation from an expected path can reveal their play preferences and skill, and help enhance our player models. Improved models will then enable in-game adaptation, to better suit individual players. In this paper we outline the basic principle of this approach and discuss the results of our first experiment.
    Proceedings of the SAB Workshop on Adaptive Approaches to Optimizing Player Satisfaction; 01/2006


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