Multilevel Darwinist Brain (MDB): Artificial Evolution in a Cognitive Architecture for Real Robots

Abstract

The multilevel Darwinist brain (MDB) is a cognitive architecture that follows an evolutionary approach to provide autonomous robots with lifelong adaptation. It has been tested in real robot on-line learning scenarios obtaining successful results that reinforce the evolutionary principles that constitute the main original contribution of the MDB. This preliminary work has lead to a series of improvements in the computational implementation of the architecture so as to achieve realistic operation in real time, which was the biggest problem of the approach due to the high computational cost induced by the evolutionary algorithms that make up the MDB core. The current implementation of the architecture is able to provide an autonomous robot with real time learning capabilities and the capability for continuously adapting to changing circumstances in its world, both internal and external, with minimal intervention of the designer. This paper aims at providing an overview or the architecture and its operation and defining what is required in the path towards a real cognitive robot following a developmental strategy. The design, implementation and basic operation of the MDB cognitive architecture are presented through some successful real robot learning examples to illustrate the validity of this evolutionary approach.