Embodied Induction: Learning External Representations

M. Wexler

The problem of inductive learning is hard, and despite much work no solution is in sight, from neural networks or other AI techniques. I suggest that inductive reasoning may be grounded in sensorimotor capacity. If an artificial system to generalize in ways that we find intelligent itshould be appropriately embodied. This is illustrated with anetwork-controlled animat that learns n-parity by representing intermediate states with its own motion. Unlike other general learning devices, such as disembodied networks, it learns from very few examples and generalizes correctly to previously unseen cases.


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