Action Selection and Operant Conditioning: A Neurorobotic Implementation

Abstract EN

Action selection (AS) is thought to represent the mechanism involved by natural agents when deciding what should be the next move or action.

Is there a functional elementary core sustaining this cognitive process? Could we reproduce the mechanism with an artificial agent and more specifically in a neurorobotic paradigm? Unsupervised autonomous robots may require a decision-making skill to evolve in the real world and the bioinspired approach is the avenue explored through this paper.

We propose simulating an AS process by using a small spiking neural network (SNN) as the lower neural organisms, in order to control virtual and physical robots.

We base our AS process on a simple central pattern generator (CPG), decision neurons, sensory neurons, and motor neurons as the main circuit components.

As novelty, this study targets a specific operant conditioning (OC) context which is relevant in an AS process; choices do influence future sensory feedback.

Using a simple adaptive scenario, we show the complementarity interaction of both phenomena.

We also suggest that this AS kernel could be a fast track model to efficiently design complex SNN which include a growing number of input stimuli and motor outputs.

Our results demonstrate that merging AS and OC brings flexibility to the behavior in generic dynamical situations.