The accumulation of adaptations in an open-ended manner during lifetime learning is a holy grail in reinforcement learning, intrinsic motivation, artificial curiosity, and developmental robotics. We present a design for a cognitive architecture that is capable of specifying an unlimited range of behaviors. We then give examples of how it can stochastically explore an interesting space of adjacent possible behaviors. There are two main novelties; the first is a proper definition of the fitness of self-generated games such that interesting games are expected to evolve. The second is a modular and evolvable behavior language that has systematicity, productivity, and compositionality, i.e. it is a physical symbol system. A part of the architecture has already been implemented on a humanoid robot. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Fernando, C., Vasas, V., & Churchill, A. W. (2013). Design for a darwinian brain: Part 2. cognitive architecture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8064 LNAI, pp. 83–95). Springer Verlag. https://doi.org/10.1007/978-3-642-39802-5_8
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