  A generalized gamification framework is introduced as a form of smart
infrastructure with potential to improve sustainability and energy efficiency
by leveraging humans-in-the-loop strategy. The proposed framework enables a
Human-Centric Cyber-Physical System using an interface to allow building
managers to interact with occupants. The interface is designed for occupant
engagement-integration supporting learning of their preferences over resources
in addition to understanding how preferences change as a function of external
stimuli such as physical control, time or incentives. Towards intelligent and
autonomous incentive design, a noble statistical learning algorithm performing
occupants energy usage behavior segmentation is proposed. We apply the proposed
algorithm, Graphical Lasso, on energy resource usage data by the occupants to
obtain feature correlations--dependencies. Segmentation analysis results in
characteristic clusters demonstrating different energy usage behaviors. The
features--factors characterizing human decision-making are made explainable.
