Developmental word acquisition and grammar learning by humanoid robots through a self-organizing incremental neural network.
We present a new approach for online incremental word acquisition and grammar learning by humanoid robots. Using no data set provided in advance, the proposed system grounds language in a physical context, as mediated by its perceptual capacities. It is carried out using show-and-tell procedures, interacting with its human partner. Moreover, this procedure is open-ended for new words and multiword utterances. These facilities are supported by a self-organizing incremental neural network, which can execute online unsupervised classification and topology learning. Embodied with a mental imagery, the system also learns by both top-down and bottom-up processes, which are the syntactic structures that are contained in utterances. Thereby, it performs simple grammar learning. Under such a multimodal scheme, the robot is able to describe online a given physical context (both static and dynamic) through natural language expressions. It can also perform actions through verbal interactions with its human partner.
He
Xiaoyuan
X
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8503, Japan. gashoenhxy@gmail.com
Ogura
Tomotaka
T
Satou
Akihiro
A
Hasegawa
Osamu
O
D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D003491:Cybernetics / Q000379:methods*; D006801:Humans; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics / Q000379:methods*; D049250:Speech Recognition Software