Task Force Chair:
Task Force Vice-Chairs:
Abbass, University of New South Wales,
Faiyaz Doctor, University of Coventry, UK
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
Christian Wagner, University of Nottingham, UK
Dongrui Wu, GE Global Research, USA
Georgios Yannakakis, IT University of Copenhagen, Denmark
Slawomir Zadrozny, Polish academy of science, Poland
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,” as initially coined by Professor R. Picard (Media Lab, MIT). 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), cognition (e.g., frustration, boredom) and motivation, as represented by for example speech, facial expressions, physiological signals, and neurocognitive performance.
Affective computing raises many new challenges for signal processing, modeling, and information aggregation. Especially, the body signals used for affect recognition are very noisy and subject-dependent. Computational intelligence (CI) methods, including fuzzy sets and systems, neural networks, and evolutionary algorithms, may be used to build intuitive and robust emotion recognition algorithms. Further, emotions, which are intrinsic to human beings, may also inspire some new CI algorithms, just like human brains inspired neural networks and survival of the fittest in nature inspired evolutionary computation.
The Affective Computing Task Force aims to promote affective and physiological computing research within the CI research community, 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.
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
· Physiological computation, i.e. the capture, analysis and automatic interpretation of physiological information
· Brain Computer Interface derived data analysis and interpretation
· 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 behavioural informatics, etc.
· Special Session (pending approval) at 5th Workshop on Brain-Machine Interfaces Systems (BMI 2015) (At SMC 2015), Hong Kong, China, October 10-13th 2015
· Special Session on Fuzzy Systems for Physiological and Affective Computing (FSPAC) (pending approval), 2015 International Conference on Fuzzy Systems (Fuzz-IEEE 2015), Istanbul, Turkey, August 2-5th 2015