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Objective Computer-based virtual coaches are increasingly being explored for patient education, counseling, and health behavior training and coaching. The objective of this research was to develop and evaluate a Virtual Mindfulness Coach for training and coaching in mindfulness meditation. Method The coach was implemented as an embodied conversational character, providing mindfulness training and coaching via mixed initiative, text-based, natural language dialogue with the student, and emphasizing affect-adaptive interaction. (The term ‘mixed initiative dialog’ refers to a human-machine dialogue where either can initiate a conversation or a change in the conversation topic.) Results Findings from a pilot evaluation study indicate that the coach-based training is more effective in helping students establish a regular practice than self-administered training using written and audio materials. The coached group also appeared to be in more advanced stages of change in terms of the transtheoretical model, and have a higher sense of self-efficacy regarding establishment of a regular mindfulness practice. Conclusion These results suggest that virtual coach-based training of mindfulness is both feasible, and potentially more effective, than a self-administered program. Of particular interest is the identification of the specific coach features that contribute to its effectiveness. Practice Implications Virtual coaches could provide easily-accessible and cost-effective customized training for a range of health behaviors. The affect-adaptive aspect of these coaches is particularly relevant for helping patients establish long-term behavior changes.

The past years have seen increasing cooperation between psychology and computer science in the field of computational modeling of emotion. However, to realize its potential, the exchange between the two disciplines, as well as the intradisciplinary coordination, should be further improved. We make three proposals for how this could be achieved. The proposals refer to: 1) systematizing and classifying the assumptions of psychological emotion theories; 2) formalizing emotion theories in implementation-independent formal languages (set theory, agent logics); and 3) modeling emotions using general cognitive architectures (such as Soar and ACT-R), general agent architectures (such as the BDI architecture) or general-purpose affective agent architectures. These proposals share two overarching themes. The first is a proposal for modularization: deconstruct emotion theories into basic assumptions; modularize architectures. The second is a proposal for unification and standardization: Translate different emotion theories into a common informal conceptual system or a formal language, or implement them in a common architecture.

Rapid growth in computational modeling of emotion and cognitive-affective architectures occurred over the past 15 years. Emotion models and architectures are built to elucidate the mechanisms of emotions and enhance believability and effectiveness of synthetic agents and robots. Despite the many emotion models developed to date, a lack of consistency and clarity regarding what exactly it means to ‘model emotions’ persists. There are no systematic guidelines for development of computational models of emotions. This paper deconstructs the often vague term ‘emotion modeling’ by suggesting the view of emotion models in terms of two fundamental categories of processes: emotion generation and emotion effects. Computational tasks necessary to implement these processes are also identified. The paper addresses how computational building blocks provide a basis for the development of more systematic guidelines for affective model development. The paper concludes with a description of an affective requirements analysis and design process for developing affective computational models in agent architectures.

Abstract.Computer games are unique elicitors of emotion. Recognition of player emotion, dynamic construction of affective player models, and modelling emotions in non-playing characters, represent challenging areas of research and practice at the crossroads of cognitive and affective science, psychology, artificial intelligence and human-computer interaction. Techniques from AI and HCI can be used to recognize player affective states and to model emotion in non-playing characters. Multiple input modalities provide novel means for measuring player satisfaction and engagement. These data can then be used to adapt the gameplay to the player's state, to maximize player engagement and to close the affective game loop.The Emotion in Games workshop (EmoGames 2011 http://sirenproject. eu/content/acii-2011-workshop-emotion-games) will bring together researchers and practitioners in affective computing, user experience research, social psychology and cognition, machine learning, and AI and HCI, to explore topics in player experience research, affect induction, sensing and modelling and affect-driven game adaptation, and modelling of emotion in non-playing characters. It will also provide new insights on how gaming can be used as a research platform, to induce and capture affective interactions with single and multiple users, and to model affect-and behaviour-related concepts, helping to operationalize concepts such as flow and engagement.The workshop will include a keynote, paper and poster presentations, and panel discussions. Selected papers will appear in a special issue of the IEEE Transactions on Affective