Using artificial intelligence to model complex systems

Artificial Intelligence (AI) research covers two main topics in relation to complex systems. The first is the identification and recognition of patterns within those systems. The second is the development of behaviour that allows an artificial organism (animat), which may be virtual or real, to interact with a complex environment, hi this thesis an approach has been taken that includes elements of both these topics, and which argues a case for learning about complex systems through interaction with them. A broad-based literature review covering the general topic of AI provides a base for the argument that (a) much of the current AI research is too restricted in scope to provide more than limited use, and (b) logic-based 'conceptual' models of AI cannot deal with the complexities of real environments. A novel neural network approach has been developed that allows generalised, self-organising learning to take place, and which can be applied both to training and to control systems. Throughout the thesis the argument for biological plausibility has been made at several points. Sensory, pattern recognition and motor systems based on modular neural network topologies are developed using genetic algorithms, and these systems are compared wherever possible to more traditional neural network techniques. A final discussion is made of the current situation in terms of machine-environment interactions, with areas that need to be developed further identified.EThOS - Electronic Theses Online ServiceGBUnited Kingdo