Choosing one option from a sequence of possibilities seen one at a time is a common problem facing agents whenever resources, such as mates or habitats, are distributed in time or space. Optimal algorithms have been developed for solving a form of this sequential search task known as the Dowry Problem (finding the highest dowry in a sequence of 100 values); here we explore whether continuous time recurrent neural networks (CTRNNs) can be evolved to perform adaptively in Dowry Problem scenarios, as an example of minimally cognitive behavior [Beer, 1996]. We show that even 4-neuron CTRNNs can successfully solve this sequential search problem, and we offer some initial analysis of how they can achieve this feat.
E. Tuci, I. Harvey and P.M. Todd (2002): Using a net to catch a mate: Evolving CTRNNs for the Dowry Problem. Proceedings of The Seventh International Conference on the Simulation of Adaptive Behavior (SAB’02), 4-9 August 2002, Edinburgh, UK. MIT Press, ISBN: 0262582171.