Uncertainty in Artificial Intelligence
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Interactive Learning from Unlabeled Instructions
Jonathan Grizou, Inaki Iturrate, Luis Montesano, Pierre-Yves Oudeyer, Manuel Lopes
Interactive learning deals with the problem of learning and solving tasks using human instruc- tions. It is common in human-robot interac- tion, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks is possible because the signals are predefined or an ad-hoc calibra- tion procedure allows to map signals to specific meanings. In this paper, we address the problem of simultaneously solving a task under human feedback and learning the associated meanings of the feedback signals. This has important practi- cal application since the user can start controlling a device from scratch, without the need of an ex- pert to define the meaning of signals or carrying out a calibration phase. The paper proposes an algorithm that simultaneously assign meanings to signals while solving a sequential task under the assumption that both, human and machine, share the same a priori on the possible instruc- tion meanings and the possible tasks. Further- more, we show using synthetic and real EEG data from a brain-computer interface that taking into account the uncertainty of the task and the signal is necessary for the machine to actively plan how to solve the task efficiently.
Pages: 290-299
PS Link:
PDF Link: /papers/14/p290-grizou.pdf
AUTHOR = "Jonathan Grizou and Inaki Iturrate and Luis Montesano and Pierre-Yves Oudeyer and Manuel Lopes",
TITLE = "Interactive Learning from Unlabeled Instructions",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "290--299"

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