Skip to Main content Skip to Navigation
Conference papers

Interactive Learning from Unlabeled Instructions

Abstract : Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, 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 calibration 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 practical application since the user can start controlling a device from scratch, without the need of an expert 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 instruction meanings and the possible tasks. Furthermore, 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.
Complete list of metadata

Cited literature [22 references]  Display  Hide  Download
Contributor : Jonathan Grizou <>
Submitted on : Tuesday, June 17, 2014 - 9:05:40 AM
Last modification on : Wednesday, January 6, 2021 - 4:03:56 PM
Long-term archiving on: : Wednesday, September 17, 2014 - 10:50:48 AM


Files produced by the author(s)


  • HAL Id : hal-01007689, version 1



Jonathan Grizou, Iñaki Iturrate, Luis Montesano, Pierre-Yves Oudeyer, Manuel Lopes. Interactive Learning from Unlabeled Instructions. UAI-30th Conference on Uncertainty in Artificial Intelligence, Jul 2014, Quebec, Canada. pp.1-8. ⟨hal-01007689⟩



Record views


Files downloads