Towards Emotion inspired Computational Intelligence (EiCI)

One of the main challenges in the computational intelligence (CI) community is to develop nature-inspired algorithms that can efficiently solve real-world problems such as the prediction of space weather phenomena. An early example in this context is taking inspiration from the biological neurons in the mammal’s nervous system and developing an artificial neuron. This work laid the foundation for artificial neural networks (ANNs) that aim to mimic the connections between neurons in the mammal’s nervous system and to develop an artificial model of the brain. ANNs are well-known CI models that have shown high generalization capability when solving real-world problems, e.g., chaotic time-series prediction problems. However, ANNs mostly tend to suffer from long computation time and high model complexity. This thesis presents a new category of CI paradigms by taking inspiration from emotions, and these CI models are referred to as emotion-inspired computational intelligence models (EiCIs). In the thesis, I have outlined the preliminary steps that have been taken to develop EiCIs. These steps include studying different emotional theories and hypotheses, designing and implementing CI models for two specific applications in artificial intelligence (prediction and optimization), evaluating the performance of the new CI models, and comparing the obtained results with the results of well-known CI models (e.g., ANNs) and discussing the potential improvement that can be achieved. The first step, and a significant contribution of this thesis, is to review the various definitions of emotions and to investigate which emotional theories that are the most relevant for developing a CI model. Amongst different theories and hypotheses of emotions, the fear conditioning hypothesis as well as affect theory have been two main sources of inspiration in the development of the EiCIs proposed in this thesis. The fear conditioning hypothesis that was first proposed by LeDoux reveals some important characteristics of the underlying neural structure of fear conditioning behavior in biological systems. Based on the features of such networks, it could be an applicable hypothesis to be the basis of the development of a subgroup of EiCIs that could be used for prediction applications, e.g. BELIMs (Brain Emotional Learning Inspired Models), and as emotion-inspired engines for decision-making applications.The second emotional theory of the thesis is the affect theory (which was first suggested by Silvan Tomkins) that describes what the basic emotions are and how they can be associated with facial expressions. A mechanism to express the basic emotional feelings is also useful in designing another category of EiCIs that are referred to as emotion-inspired optimization methods. The fundamental hypotheses of the thesis, have led to developing EiCIs, can be presented as follows. The first hypothesis is that the neural structure of fear conditioning can be considered to be a nature-based system with the capability to show intelligent behavior through its functionality. This hypothesis is stated on the basis of the three main characteristics of the neural structure of fear conditioning behavior.The first characteristic is that the amygdala is the main center for processing fear-induced stimuli and that it provides the fear reaction through its interaction with other regions of the brain such as the sensory cortex, the thalamus, and the hippocampus. The second characteristic is that the procedure of processing of fearful stimuli and the provision of emotional reactions is simple and quick. The third aspect is that the amygdala not only provides fear responses but also learns to predict aversive events by interacting with other regions of the brain, which means that an intelligent behavior emerges.The second hypothesis is that the system in which the three monoamines neurotransmitters serotonin, dopamine, and noradrenalin and thus produces emotional behaviors, can be viewed as a biological system associated with the emergence of intelligent behavior.The above hypotheses state that a suitable way to develop new CI models is to take inspiration from the neural structure of fear conditioning and the natural system of three monoamine neurotransmitters. A significant contribution of this thesis is the evaluation of the ability of EiCIs by examining them to solve real-world problems such as the prediction of space weather phenomena (e.g., predicting real time-series such as sunspot number, auroral electrojet index, and disturbance time index) and the optimization of some central procedures in network communications. These evaluations have led to that comparable results have been obtained, which in turn supports the conclusion that EiCIs have acceptable and reasonable performance regarding computation time and model complexity. However, to achieve the final goal of the research study (i.e., to develop a CI model with low computation time and low model complexity), some enhancements of EiCIs are necessary. Moreover, new designs and implementations of these models can be developed by taking inspiration from other theories