*Title: Machine Learning for Adaptive Language interaction with an intelligent
dialog companion
*Keywords: Deep reinforcement learning, interactive machine learning, natural
language processing, neural networks, intelligent dialog companion
*Context: The thesis takes place in the context of a collaboration between the
Robot Cognition Laboratory (INSERM/CNRS), the innovation services department of
Intrinsic Cloud Temple, and the Flowers laboratory (Inria).
*Background: Based on RCL experience in human-robot cooperation and learning,
we have developed an intelligent agent that accompanies humans in their
experience using web-based tools, learns from explicit demonstration, and
allows the user to re-use and recompose learned plans to form more complex
structured plans.
*Project: For the moment, the system learns from demonstration. The project
will extend and study algorithms for interactive machine learning (using Deep
RL) to include language instructions as one of the ways that the system can
learn, that is, by using the invariant structure of the human verbal
description to organize procedural representations. This will advance the
state of the art in the integration of language into reinforcement learning
systems.
* Location: Lyon and Nanterre, France (Inserm and Cloud Temple)
* Response modalities: Send (1) a letter of motivation stating your interest
in the project, (2) a CV, and (3) two letters of recommendation to the three
addresses for Jean-Michel Dussoux, Peter Ford Dominey, and Pierre-Yves Oudeyer
listed in the call, with the subject [Intelligent Assistant PhD].
j...@intrinsec.com; pierre-yves.oude...@inria.fr; peter.domi...@inserm.fr
More details:
https://www.dropbox.com/s/g5ksylrrlgatkf2/RCL-CT-THESE-interaction-manager-finalV2.pdf?dl=0
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