Top Down and Bottom Up Interactions between Low Level Reactive Control and Symbolic Rule Learning in Embodied Agents
Mammals bootstrap their cognitive structures through embodied interaction with the world. This raises the question: how the reactive control of initial behavior is recurrently coupled to the high level symbolic representations they give rise to? We investigate this question in the framework of the "Distributed Adaptive Control" (DAC) cognitive architecture, where we study top down and bottomup interactions between low level reactive control and symbolic rule learning in embodied agents. Reactive behaviors are modeled using a neural allostatic controller, whereas high level behaviors are modeled using a biologically grounded memory network reflecting the role of the prefrontal cortex. The interaction of these modules in a closed loop fashion suggests how symbolic representations might have been shaped from low level behaviors and recruited for behavior optimization.
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