no code implementations • 2 Dec 2021 • Shashi Suman, Francois Rivest, Ali Etemad
In this paper, we propose a Bayesian Reinforcement learning framework that can approximate the current occupant state in a partially observable smart home environment using its thermal preference, and then identify the occupant as a new user or someone is already known to the system.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 14 Jun 2021 • Jason Zwicker, Francois Rivest
It is commonly assumed that in order to achieve the statistics with a pacemaker accumulator model it is necessary to have start and stop thresholds.
no code implementations • 4 Jun 2021 • Gabriele Cimolino, Francois Rivest
Animals can quickly learn the timing of events with fixed intervals and their rate of acquisition does not depend on the length of the interval.
no code implementations • 26 Feb 2021 • Shashi Suman, Ali Etemad, Francois Rivest
We then investigate the possibility of human behavior being altered as a result of the smart home and the human model adapting to one-another.
1 code implementation • 11 Mar 2011 • Francois Rivest, Yoshua Bengio
We provide an analytical proof that the model can learn inter-event intervals in a number of trials independent of the interval size and that the temporal precision of the system is proportional to the timed interval.