  A profound challenge for A-Life is to construct agents whose behavior is
'life-like' in a deep way. We propose an architecture and approach to
constructing networks driving artificial agents, using processes analogous to
the processes that construct and sculpt the brains of animals. Furthermore the
instantiation of action is dynamic: the whole network responds in real-time to
sensory inputs to activate effectors, rather than computing a representation of
the optimal behavior and sending off an encoded representation to effector
controllers. There are many parameters and we use an evolutionary algorithm to
select them, in the context of a specific prey-capture task. We think this
architecture may be useful for controlling small autonomous robots or drones,
because it allows for a rapid response to changes in sensor inputs.
