With this chapter, we aim at giving guidance for other culture-specific generation approaches by providing a hybrid methodology to build culture-specific computational models as well as potential approaches for their evaluation. Secondly we performed a perception study with virtual characters whose behaviour is driven by the calculations of the network and are rated by members of the German and Japanese cultures. The model is validated in two ways: With a cross-fold validation we estimate the power of the network by predicting behaviours for parts of the recorded data that were not used to train the network. While the model is structured based on cultural theories and theoretical knowledge on their influence on prototypical behaviour, the parameters of the model are learned from a multimodal corpus recorded in the German and Japanese cultures. Based on our former work, we present a hybrid approach of integrating culture into a Bayesian Network that aims at predicting culture-dependent non-verbal behaviours for a given conversation. Theory-based approaches that build computational models based on cultural theories to predict culture-dependent behaviours, and data-driven approaches that rely on multimodal recordings of existing cultures. To incorporate culture into intelligent systems, there are two approaches that are commonly proposed. A user study was conducted to evaluate the effects of the emotional virtual agents in different aspects, including task completion, time spent in conversation, following distance, and realism of the emotional virtual crowd. We adopt a simple 2D mood model and use icons to represent the mood states of the agents. This paper studies the effects of a virtual crowd with emotion on task completion in a virtual market. We conducted a user study to evaluate the effects of the emotional virtual agents in different aspects, including task completion, time spent in conversation, following distance, and realism of the emotional virtual crowd. The mood model supports four mood states, which are “Excitement”, “Anger”, “Sadness”, and “Neutral”. Furthermore, their moods are also affected by the conversational responses of the users. The emotional agents' moods are affected by their own interaction (e.g., collision) while they are walking. The users communicate with the virtual agents via a conversation dialog. Then, the users can buy the required items. The users have to interact with the virtual agents so that the virtual agents are willing to lead the users to specific vendors. The customization of behavioral characteristics, such as how the character gazes, how fast it gesticulates or what position and orientation it adopts during an interaction, is usually. For example, even though most MMORPGs (Massive Multiplayer Online Role Playing Games) allow the player to customize his or her character, this customization is only aesthetical, like clothing or hair style (Figure 1). This shortcoming can also be observed in modern computer games where interactions between game characters follow a repetitive and predictable pattern which often leads to player immersion loss. However, software tools which provide such features often lack the flexibility and adaptability the researchers require. Virtual simulations of social behavior can help visualize and analyze theoretical findings and assumptions in various sociological domains. These were then tested in an evaluation study. For demonstration purposes, we designed four behavioral profiles that simulate prototypical behaviors for differences in personality and gender. To this end, we present a software framework which is able to visualize individualized non-verbal behavior. In this paper, we describe an approach to individualizing the non-verbal behavior of virtual agents. However, most systems only provide customization for visual features of the characters. Individualized virtual agents can enhance the user’s perception of a virtual scenario.
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