Social learning in Population-based Adaptive Systems

The subject of the present investigation is population-based adaptive systems (PAS), as implemented in the NEW TIES platform. In many existing PASs two adaptation mechanisms are combined, (non-Lamarckian) evolution and individual learning, inevitably raising the issue of dasiaforgetful populationspsila: individually learned knowledge disappears when the individual that learned it dies. We propose social learning by explicit knowledge transfer to overcome this problem. Our mechanism is based on 1) direct communication among agents in the population, 2) messages carrying rules that the sender agent uses in its controller, and 3) the ability of the recipient agent to incorporate foreign rules into its controller. Thus, knowledge can be disseminated and multiplied within the same generation, making the population a knowledge reservoir for individually acquired knowledge. We present an initial assessment of this idea and show that this social mechanism is capable of efficiently distributing knowledge and improving the performance of the population.