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The advantages of gestures, for example chunking command and operand into a single action enabling a higher input bandwidth or being more fun, are known. However, gestures require an investment of time to be learned and are, in spite of their advantages, underrepresented in interfaces. This project tries to tackle the shortcomings by developing techniques that support the learning of gestures on touchscreen devices using multimodal feedback.
The project extends previous work. Where previous work relied on visual feedback, tactile and auditory feedback should now be included as well. Moreover, the evaluation of the system should be carried out in multiple sessions to be able to assess learning effects more reliably than it was did in single-session evaluations of the existing efforts.
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This paper presents a model for turn management that relies on statecharts and discusses how emotions can influence the turn taking behavior of an agent. The fi rst part of the paper develops an interaction state model that is used to keep track of the state of the interaction. As a means to test the model, we propose dialog patterns that are described later on. In the second
part, the influence of three basic emotions on the turn taking behavior of an agent is investigated. A concept to store and retrieve speaking actions is described. The closing chapter discusses the architecture that was used to implement a demonstration system with the developed functionalities. |
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