“…Because the SSA is part of the active adaptive perception implementation, it is expected that the current implementation is suitable for real-time environments. Additionally the proposed architecture is a novel method for composing training and construction algorithms for neural networks, it can evolve a network in non-episodic environments, unlike Topology and Weight Evolving Artificial Neural Networks such as NEAT (Stanley & Miikkulainen, 2002), and compared to Constructive Neural networks (Sharma & Chandra, 2010;Vamplew & Ollington, 2005;Lahnajarvi et al, 2002;Fanguy & Kubat, 2002;Parekh et al, 2000;Fahlman & Lebiere, 1990;Frean, 1990;Ring, 1997) the architecture does not need heuristic criteria for updating and is suitable for reinforcement learning. Lastly, the architecture for active adaptive perception has demonstrated features typically associated with continual learning, particularly (a) the ability to learn in a single lifetime with no known terminal states, and (b) the ability to learn how to learn incrementally.…”