Simple cognitive systems are mostly ruled by preset stimuli-reaction rules. E.g. an earthworm will automatically try to meander towards darkness. Universal principles, i.e. algorithms applicable to a wide range of different environmental settings, become however predominant in highly developed cognitive systems. We humans, to give an example, are constantly, and most of the time unconsciously trying to predict the outcome of actions and movements taking place in the world around us, even if these outcomes are not directly relevant for our intentions at the given time, allowing us to extract regularities in the observed processes for possible later use. Technically this attitude corresponds to a time-series prediction-task which is quite universal in its applicability. We use it, e.g., to obtain unconsciously knowledge on the ways a soccer ball rolls and flies as well as to extract from the sentences we listen-to the underlying grammatical rules of our mother-tongue.
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).