Hierarchical Latent Attribute Models (HLAMs) are a family of discrete latent variable models that attracting increasing attention in educational, psychological, and behavioral sciences. The key ingredients an HLAM include binary structural matrix directed acyclic graph specifying hierarchical constraints on the configurations attributes. These components encode practitioners' design information...

Abstract Markov decision processes are a ubiquitous formalism for modelling systems with non-deterministic and probabilistic behavior. Verification of these models is subject to the famous state space explosion problem. We alleviate this problem by exploiting hierarchical structure repetitive parts. This not only occurs naturally in robotics, but also programs describing, e.g., network protocol...