Interactive Learning from Unlabelled Instructions

Jonathan Grizou, Luis Montesano Universidad de Zaragoza, Iñaki Iturrate, Manuel Lopes
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:608-617, 2014.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-grizou14a, title = {Interactive Learning from Unlabelled Instructions}, author = {Grizou, Jonathan and de Zaragoza, Luis Montesano Universidad and Iturrate, I{\~n}aki and Lopes, Manuel}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {608--617}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/grizou14a/grizou14a.pdf}, url = {https://proceedings.mlr.press/r12/grizou14a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Interactive Learning from Unlabelled Instructions %A Jonathan Grizou %A Luis Montesano Universidad de Zaragoza %A Iñaki Iturrate %A Manuel Lopes %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-grizou14a %I PMLR %P 608--617 %U https://proceedings.mlr.press/r12/grizou14a.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Grizou, J., de Zaragoza, L.M.U., Iturrate, I. & Lopes, M.. (2014). Interactive Learning from Unlabelled Instructions. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:608-617 Available from https://proceedings.mlr.press/r12/grizou14a.html. Reissued by PMLR on 04 October 2026.

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