Abstract
The rebirth of complex systems in several distinct domains of research has posed new epistemic questions. Self-organizing systems as well as autonomous robots have a tendency to not only behave in an unpredictable way, they are also extremely difficult to analyse. In this paper we discuss three problems with neural networks that are important for complex systems in general. They are related to the proper design of a self-organizing system, to the role of the system engineer, and to the proper explanation of system behavior. We present a generally applicable solutions, which is based on a “symbol grounding” neural network architecture. We also take a look at an implemented network which hints at the fact that grounding in this case must involve teleological terms. We then discuss the relation of this approach to the measurement problem in physics and point out similarities to existing positions in philosophy. However, it should be noted that our “solution” of the explanation problem may be judged as being a very sceptic one.
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Prem, E. (1995). Understanding Complex Systems: What Can the Speaking Lion Tell us?. In: Steels, L. (eds) The Biology and Technology of Intelligent Autonomous Agents. NATO ASI Series, vol 144. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79629-6_22
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DOI: https://doi.org/10.1007/978-3-642-79629-6_22
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