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To produce emotional artificial systems in AI domain, usually a subset of human emotional states are imported to the target domain and the major differences between natural and artificial domains are often ignored. In this paper we will discuss about why such an approach is not useful for all possible applications of emotions and we will show how it is necessary and possible to produce artificial emotion systems based on the target systems goals, abilities and needs  

There are several approaches to emotions in AI, most of which are inspired by human emotional states and their arousal mechanisms. These approaches usually use high-level models of human emotions that are too complex to be directly applicable in simple artificial systems. It seems that a new approach to emotions, based on their functional role in information processing in mind, can help us to construct models of emotions that are both valid and simple. In this paper, we will try to present a model of emotions based on their role in controlling the attention. We will evaluate the performance of the model and show how it can be affected by some structural and environmental factors. © 2008 The... 

One of the main issues in the field of social and cognitive robotics is the robot’s ability to recognize emotional states and emotional interaction between robots and humans. Through effective emotional interaction, robots will be able to perform many tasks in human society. In this research, we have developed a robotic platform and a vision system to recognize the emotional state of the user through its facial expressions, which leads to a more realistic humanrobot interaction (HRI). First, a number of features are extracted according to points detected by a vision system from the face of the user. Then, the emotional state of the user is analyzed with the help of these features. For the... 

In recent years, the regression dynamic causal modelling (rDCM) method was introduced as a new version of dynamic causal modelling (DCM) to derive effective connectivity in whole-brain networks for functional magnetic resonance imaging (fMRI) data. In this research, we used data obtained while applying the stimulation of audio movie comprised different emotional states. We applied this method to two networks consisting of ten auditory and forty-four regions, respectively. This method was used to study effective connections between emotional states and represent the distinction between emotions. Finally, significant effective connections were found in emotional processing and auditory...