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What is Complex System Theory

Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence
Complex system theory deals with `complex’ dynamical systems, viz with dynamical systems containing a very large number of interacting dynamical variables. Preeminent examples of complex systems are the gen-regulation network at basis of all living, self-organizing phase transitions in physics like superconductivity and magnetism, and cognitive systems, the later being the most sophisticated and probably also the least understood of all complex dynamical systems.
Published in Chapter:
Emotions, Diffusive Emotional Control and the Motivational Problem for Autonomous Cognitive Systems
C. Gros (J.W. Goethe University Frankfurt, Germany)
DOI: 10.4018/978-1-60566-354-8.ch007
Abstract
All self-active living beings need to solve the motivational problem—the question of what to do at any moment of their life. For humans and non-human animals at least two distinct layers of motivational drives are known, the primary needs for survival and the emotional drives leading to a wide range of sophisticated strategies, such as explorative learning and socializing. Part of the emotional layer of drives has universal facets, being beneficial in an extended range of environmental settings. Emotions are triggered in the brain by the release of neuromodulators, which are, at the same time, are the agents for meta-learning. This intrinsic relation between emotions, meta-learning and universal action strategies suggests a central importance for emotional control for the design of artificial intelligences and synthetic cognitive systems. An implementation of this concept is proposed in terms of a dense and homogeneous associative network (dHan).
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