Affective Computing Task Force

Task Force Chair:

Dongrui Wu
Machine Learning Lab, GE Global Research, USA
drwu09@gmail.com
website

Task Force Vice-Chair:

Christian Wagner
School of Computer Science, University of Nottingham, UK
Christian.Wagner@nottingham.ac.uk
website

Task Force Members:

Hani Hagras, University of Essex, UK
Kostas Karpouzis, National Technical University of Athens, Greece
Annabel Latham, Manchester Metropolitan University, UK
Marie-Jeanne Lesot, LIP6-UPMC, France
Peter Lewis, University of Birmingham, UK
Chin-Teng Lin, National Chiao Tung University, Taiwan
Gracian Trivino, European Center for Soft Computing, Spain
Georgios Yannakakis, IT University of Copenhagen, Denmark
Slawomir Zadrozny, Polish academy of science, Poland

Motivation:

Computational intelligence is a set of Nature-inspired computational methodologies and approaches to address complex real world problems to which traditional methodologies and approaches (first principles, probabilistic, black-box, etc.) are ineffective or infeasible. It includes neural networks, fuzzy logic systems, evolutionary computation, swarm intelligence, chaos theory, etc.

Affective computing is computing that relates to, arises from, or deliberately influences emotions. It has been gaining popularity rapidly in the last decade because it has great potential in the next generation of human-computer interfaces. One goal of affective computing is to design a computer system that responds in a rational and strategic fashion to real-time changes in user affect (e.g., happiness, sadness, etc), cognition (e.g., frustration, boredom, etc) and motivation, as represented by speech, facial expressions, physiological signals, neurocognitive performance, etc.

Affective computing raises many new challenges for signal processing and machine learning. Especially, the body signals used for affect recognition are very noisy and subject-dependent. Computational intelligence methods, particularly fuzzy logic systems, may be used to build intuitive and robust emotion recognition algorithms. On the other hand, emotions, which are intrinsic to human beings, may also inspire some new computational intelligence algorithms, just like how human brains inspired neural networks and population-based sexual evolution through reproduction of generation inspired evolutionary computation.

Goals:

The Affective Computing Task Force aims to promote affective computing research within the CIS, especially, to study how computational intelligence algorithm can be used to solve challenging affective computing problems, and how affects (emotions) can inspire new computational intelligence algorithms. We also try to bring together the CIS and the HUMAINE association, which is so far the largest affective computing research community in the world.

Scope:

The scope of this task force includes, but is not limited to:

Emotion-inspired computational intelligence algorithms
Computational models and architecture for processing emotions and other affective states
Automatic emotion recognition & synthesis from physiological signals, facial expressions, body language, speech, or neurocognitive performance
Emotion mining from texts, images, or videos
Affective interaction with virtual agents and robots
Applications of affective computing in interactive learning, affective gaming, personalized robotics, virtual reality, social networking, smart environments, healthcare and behavioral informatics, etc.

Planned Activities:

Publications in the Computational Intelligence Magazine on Affective Computing to introduce this relatively new research area to the CIS.
Organize a special session on “Computational Intelligence and Affective Computing” at WCCI 2012.
Help promote the Workshop on “Affective Computational Intelligence” at SSCI 2013.
Organize special sessions at the 2013 Affective Computing and Intelligent Interaction (ACII) conference, the bi-annual HUMAINE event.