Specifying complex systems with bayesian programming. An alife application

Abstract

One of the most important application areas of Artificial Life is the simulation of complex processes. This paper shows how to use Bayesian Programming to model and simulate an artificial life problem: that of a worm trying to live in a world full of poison. Any model of a real phenomenon is incomplete because there will always exist unknown, hidden variables that influence the phenomenon. To solve this problem we apply a new formalism, Bayesian programming. The proposed worm model has been used to train a population of worms using genetic algorithms. We will see the advantages of our method compared with a classical approach. Finally, we discuss the emergent behaviour patterns we observed in some of the worms and conclude by explaining the advantages of the applied method. It is this characteristic (the emergent behaviour) which makes Artificial Life particularly appropriate for the study and simulation of complex systems for which detailed analysis, using traditional methods, is practically non-viable. © Springer-Verlag Berlin Heidelberg 2005.