Biological cognitive systems are `autonomous’, viz they decided by themselves what to do. Highly developed cognitive systems, like the one of mammals, regularly respond to sensory stimuli and information but are generally not driven by the incoming sensory information, i.e. the sensory information does not force them to any specific action. The motivational problem then deals with the central issue of how a highly developed cognitive system selects its actions and targets. This is the domain of instincts and emotions, even for humans. Note, that rational selection of a primary target is impossible, rational and logical reasoning being useful only for the pursue of primary targets set by the underlying emotional network. Most traditional research in artificial intelligence disregards the motivational problem, assuming internal primary goal selection is non-essential and that explicit primary target selection by supervising humans is both convenient and sufficient.
Published in Chapter:
Emotions, Diffusive Emotional Control and the Motivational Problem for Autonomous Cognitive Systems
C. Gros (J.W. Goethe University Frankfurt, Germany)
Copyright: © 2009
|Pages: 14
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).