Computational intelligence for structured learning of a partner robot based on imitation
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent embedded agents
MESO: Supporting Online Decision Making in Autonomic Computing Systems
IEEE Transactions on Knowledge and Data Engineering
Relational Learning by Imitation
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Computational intelligence for structured learning of a partner robot based on imitation
Information Sciences: an International Journal
Visual perception and reproduction for imitative learning of a partner robot
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
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Imitative learning has recently piqued the interest of various fields including neuroscience, cognitive science and robotics. In computational behavior modeling and development, it promises an accessible framework for rapidly forming behavior models without tedious supervision or reinforcement. Given the availability of low-cost wearable sensors, the robustness of real-time perception algorithms and the feasibility of archiving large amounts of audio-visual data, it is possible to unobtrusivelyarchive the daily activities of a human teacher and his responses to external stimuli.We combine this data acquisition/representation process with statistical learning machinery (hidden Markov models) as well as discriminative estimation algorithms to form a behavioral model of a human teacher directly from the data set.The resultingsystem learns audio-visual interactive behavior from the human and his environment to produce an interactive autonomous agent.The agent subsequently exhibits simple audio-visual behaviors that appear coupled to real-world test stimuli.