  We aim to investigate the potential impacts of smart homes on human behavior.
To this end, we simulate a series of human models capable of performing various
activities inside a reinforcement learning-based smart home. 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. We design a semi-Markov
decision process human task interleaving model based on hierarchical
reinforcement learning that learns to make decisions to either pursue or leave
an activity. We then integrate our human model in the smart home which is based
on Q-learning. We show that a smart home trained on a generic human model is
able to anticipate and learn the thermal preferences of human models with
intrinsic rewards similar to the generic model. The hierarchical human model
learns to complete each activity and set optimal thermal settings for maximum
comfort. With the smart home, the number of time steps required to change the
thermal settings are reduced for the human models. Interestingly, we observe
that small variations in the human model reward structures can lead to the
opposite behavior in the form of unexpected switching between activities which
signals changes in human behavior due to the presence of the smart home.
