In many settings, episodic demonstrations provide a natural and robust mechanism to partially specify a task, even in the presence of unlabeled demonstration errors. This problem, inferring intent from demonstrations, has received a fair amount of attention over the past two decades particularly within the robotics and AI communities; but until recently has remained unexplored for learning concept classes such as temporal logic and automata. In this talk, I will review a promising thread of research that adapts maximum (causal) entropy inverse reinforcement learning to estimate the posteriori probability of a bounded trace property given a multi-set of demonstrations.