Interactive Learning from Unlabeled Instructions

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.