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Interactive Learning from Unlabeled Instructions
Jonathan Grizou, Inaki Iturrate, Luis Montesano, Pierre-Yves Oudeyer, Manuel Lopes
Abstract:
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.
Keywords:
Pages: 290-299
PS Link:
PDF Link: /papers/14/p290-grizou.pdf
BibTex:
@INPROCEEDINGS{Grizou14,
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)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "290--299"
}
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