Architectures for Modeling Emotion: Cross-Disciplinary Foundations
Papers from the AAAI Spring Symposium
Eva Hudlicka, and Lola Cañamero, Cochairs
Recent years have witnessed increased interest in modeling emotion within cognitive and behavior-based (software and robotic) agent architectures and cognitive models of human behavior. This interest results in part from advances in agent technology, cognitive neuroscience and emotion research that make such models possible, and in part from maturing applications that require or benefit from the inclusion of different emotion-related aspects (e.g., adaptive human-computer interfaces, social and expressive robots, autonomous agents, decision support systems, etc).
This surge of interest has led to a number of emotion-based architectures and applications. However, this work is often carried in an "ad hoc" manner since, due to the short history of the field and the lack of appropriate frameworks for common reflection, there is a still very limited understanding of the mechanisms underlying such architectures, and of standards for a sound validation practice. Researchers in this area increasingly perceive the need to move in this direction to make work in the field progress beyond mere engineering applications and towards a more scientific discipline. The aim of the proposed symposium is to take a step in this direction.
The objective of this symposium is to provide a multidisciplinary forum focusing on mechanisms underlying agent architectures that include or emphasize emotion. In particular, we focus on two aspects not contemplated in previous symposia and workshops: validation of emotion models and architectures, and relevance of recent findings from affective neuroscience research, in addition to existing research in psychology. We wish to explore the ways in which neuroscience and psychology results can motivate and inform the design of emotion models and architectures, constrain specific mechanisms and processes within these models, serve as a source of data for model and architecture validation, and benefit from the feedback provided by computational models and tools.