Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Yuuya Sugita, Jun Tani
We present a novel connectionist model for acquiring the semantics of a simple language through the behavioral experiences of a real robot. We focus on the “compositionality” of semantics, a fundamental character- istic of human language, which is the ability to understand the meaning of a sentence as a combination of the meanings of words. We also pay much attention to the “embodiment” of a robot, which means that the robot should acquire semantics which matches its body, or sensory-motor system. The essential claim is that an embodied compositional semantic representation can be self-organized from generalized correspondences between sentences and behavioral patterns. This claim is examined and conﬁrmed through simple experiments in which a robot generates corre- sponding behaviors from unlearned sentences by analogy with the corre- spondences between learned sentences and behaviors.