I was reading about the grounding problem after seeing it mentioned in another answer today. The article states that, in order to avoid the "infinite regress" of defining all words with other words, we must ground the meaning of some words in the "sensorimotor."
To be grounded, the symbol system would have to be augmented with nonsymbolic, sensorimotor capacities—the capacity to interact autonomously with that world of objects, events, actions, properties and states that its symbols are systematically interpretable (by us) as referring to.
Obviously, this made me think of Reinforcement Learning. But I'm not exactly sure what counts as "interaction." Would this necessarily imply an MDP-like formulation with rewards, state transitions, etc? Or could some form of grounding be accomplished with supervised learning?
This seems like a pretty fundamental problem of AI. Does anyone know of research being done on grounding words/symbols within an RL agent?