ROBOTIC MACHINE LEARNING OF ANAPHORA

Our contribution tackles the problem of learning to understand anaphor ic references in the context of robotic machine learning; e.g. Get the large screw. Put it in the left hole. Our solution assumes the probab ilistic theory of learning spelt out in earlier publications. Associat ions are formed probabilistically between constituents of the verbal c ommand and constituents of a presupposed internal representation. The theory is extended, as a first step, to anaphora by learning how to di stinguish between incorrect surface depth and the correct tree-structu re depth of the anaphoric references.