In our first approach, we model user interaction as a continuous game between non-cooperative players. Game theoretic analysis often relies on the assumption that the utility function of each agent is known a priori; however, this assumption usually does not hold in many real-world applications. We propose a parametric utility learning framework leveraging inverse optimization techniques and explore vulnerability from adversarial attacks in utility learning and present potential security risks. A generalized robust framework of the proposed learning method is introduced by employing constrained feasible generalized least squares estimations with heteroskedastic inference. We further develop the theoretical formulation of a new parametric utility learning method that uses a probabilistic interpretation---i.e.~a mixture of utilities---of agent utility functions that allows us to account for variations in agents' parameters over time.
Advancements in cyber-physical systems lead to the collection of more and more data as a result of users' interactions with cyber-physical systems' sensing/actuation platforms. This enables new ways to improve infrastructure systems and lead to smart-building energy efficiency. Towards modeling users in their engagement and integration in a Human-Centric Cyber-Physical System, we characterize their interaction as a sequential discrete game between non-cooperative players. We propose the design and implementation of a large-scale network gamification application with the goal of improving the energy efficiency of a building through the utilization of cutting-edge Internet of Things (IoT) sensors and cyber-physical systems sensing/actuation platforms. Then, by observing human decision-makers and their decision strategies in their operation of building systems, we can apply inverse learning techniques in order to estimate their utility functions. We propose a benchmark utility learning framework that employs robust estimations for classical discrete choice models provided with high dimensional imbalanced data. To improve forecasting performance, we extend the benchmark utility learning scheme by leveraging Deep Learning end-to-end training with Deep bi-directional Recurrent Neural Networks.