Online Learning in Perception-Action Systems

In this position paper, we seek to extend the layered perception-action paradigm for on-line learning such that it includes an explicit symbolic processing capability. By incorporating symbolic processing at the apex of the perception action hierarchy in this way, we ensure that abstract symbol manipulation is fully grounded, without the necessity of specifying an explicit representational framework. In order to carry out this novel interfacing between symbolic and sub-symbolic processing, it is necessary to embed fuzzy first-order logic theorem proving within a variational framework. The online learning resulting from the corresponding Euler-Lagrange equations establishes an extended adaptability compared to the standard subsumption architecture. We discuss an application of this approach within the field of advanced driver assistance systems, demonstrating that a closed-form solution to the Euler Lagrange optimization problem is obtainable for simple cases.