Journal article

Emergent value orientation in self-organization of an animat

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Krebs, F.; Bossel, H.
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Ecological Modelling
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The genetic algorithms proposed by Holland [Holland, J.H., 1992/1975. Adaptation in Natural and Artificial Systems. MIT Press Cambridge] have previously been used in computer experiments to study knowledge growth in simple artificial animal models ('animats') acting in a simulated environment. In the present paper, this work is extended to include the concepts of orientation theory [Bossel, H., 1977. Orientors of nonroutine behavior. In: H. Bossel (Editor), Concepts and Tools of Computer-Assisted Policy Analysis. Birkhauser Verlag, Basel, pp. 227-265]. Orienters are value orientations emerging in the evolutionary adaptation of systems to their specific environments. The animat experiments demonstrate how knowledge growth during the learning phase leads to balanced attention to basic needs, i.e. satisfaction of basic orientors (effectiveness, freedom of action, security, adaptability in addition to existence and coexistence needs) with some variation of emphasis among individuals in a population. The results of the computer experiments suggest that (multidimensional) value orientation is a basic emergent feature of evolutionary adaptation to environments characterized by sparse resources, variety, fluctuation, and change. Pathological behavior and system failure must be expected if there is insufficient attention to any of the basic orientors. Apart from these basic insights, the approach employed here can be applied more generally to comprehensive assessments of system fitness and performance, and in particular to comparative studies of the feasibility and viability of future development paths. (C) 1997 Elsevier Science B.V.

genetic algorithms classifier systems animat orientors learning, life, value orientation future studies artificial intelligence artificial


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