Affective computing aims at developing computers with understanding capabilities vastly beyond today’s computer systems. Affective computing is computing that relates to, or arises from, or deliberately influences emotion. Affective computing also involves giving machines skills of emotional intelligence: the ability to recognize and respond intelligently to emotion, the ability to appropriately express (or not express) emotion, and the ability to manage emotions. The latter ability involves handling both the emotions of others and the emotions within one self.
Today, more than ever, the role of computers in interacting with people is of importance. Most computer users are not engineers and do not have the time or desire to learn and stay up to date on special skills for making use of a computer’s assistance. The emotional abilities imparted to computers are intended to help address the problem of interacting with complex systems leading to smoother interaction between the two. Emotional intelligence that is the ability to respond to one’s own and others emotions is often viewed as more important than mathematical or other forms of intelligence. Equipping computer agents with such intelligence will be the keystone in the future of computer agents.
Emotions in people consist of a constellation of regulatory and biasing mechanisms, operating throughout the body and brain, modulating just about everything a person does. Emotion can affect the way you walk, talk, type, gesture, compose a sentence, or otherwise communicate. Thus, to infer a person’s emotion, there are multiple signals you can sense and try to associate with an underlying affective state. Depending on which sensors is available (auditory, visual, textual, physiological, biochemical, etc.) one can look for different patterns of emotion’s influence. The most active areas for machine motion recognition have been in automating facial expression recognition, vocal inflection recognition, and reasoning about emotion given text input about goals and actions. The signals are then processed using pattern recognition techniques like hidden Markov models (HMM’s), hidden decision trees, auto-regressive HMM’s, Support Vector Machines and neural networks.
The response of such an affective system is also very important consideration. It could have a preset response to each user emotional state or it could learn from trying out different strategies on the subject with the passing of time and deciding the best option as time passes on. user, to see which are most pleasing. Indeed, a core property of such learning systems is the ability to sense positive or negative feedback – affective feedback – and incorporates this into the learning routine. A wide range of uses have been determined and implemented for such systems. These include systems, which detect the stress level in car drivers to toys, which sense the mood of the child and reacts accordingly.
In the following report I will be exploring the different aspects of affective computing and the current research, which is going on about affective computing.