Applying evolvable hardware to autonomous agents

Abstract

In this paper, we describe a parallel processing architecture for Evolvable Hardware (EHW) which changes its own hardware structure in order to adapt to the environment in which it is embedded. This adaptation process is a combination of genetic learning with reinforcement learning. As an example of EHW applications, the arbitration in behavior-based robot is discussed. Our goal by implementing adaptation in hardware is to produce a flexible and fault-tolerant architecture which responds in real-time to a changing environment.