Circuits of Production Rule GenNets

Abstract

A year ago, the author evolved a simulated artificial creature (biot, i.e. biological robot), called LIZZY, which consisted of fully connected neural network modules (called GenNets), whose weights were evolved such that each GenNet performed some desired behavior, such as making the biot walk straight ahead, turn clockwise or anticlockwise, peck, or mate [de Garis 1990, 1991, 1993]. Other such GenNets were evolved to detect sinusoidal frequencies, or signal strengths, or signal strength differences, etc. However, the middle layer, between the detector and the motion GenNets, was implemented with traditional symbolic production rules, for reasons of computer simulation speed. Every time a GenNet was added to the system, simulation speed dropped. This paper “completes” the above work, by proposing a model which shows how a circuit of production-rule-GenNets (i.e. GenNets which behave like production rules), can be evolved which implements the middle or “decisional” layer, which takes signals outputted from the detector GenNets, and then decides which motion GenNet should be switched on. This work takes a first step towards the evolution of whole nervous systems, where circuits of GenNet modules (of appropriate types) are evolved to give a biot a total behavioral performance repertoire.