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Computational Analytical Framework for Affective Modeling: Towards Guidelines for Designing Computational Models of Emotions
Abstract
Computational affective models are being developed both to elucidate affective mechanisms, and to enhance believability of synthetic agents and robots. Yet in spite of the rapid growth of computational affective modeling, no systematic guidelines exist for model design and analysis. Lack of systematic guidelines contributes to ad hoc design practices, hinders model sharing and re-use, and makes systematic comparison of existing models and theories challenging. Lack of a common computational terminology also hinders cross-disciplinary communication that is essential to advance our understanding of emotions. In this chapter the author proposes a computational analytical framework to provide a basis for systematizing affective model design by: (1) viewing emotion models in terms of two core types: emotion generation and emotion effects, and (2) identifying the generic computational tasks necessary to implement these processes. The chapter then discusses how these computational ‘building blocks' can support the development of design guidelines, and a systematic analysis of distinct emotion theories and alternative means of their implementation.
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