Plan recognition is the problem of inferring an actor’s plan by watching the actor’s actions and their effects. Our work was the first to model this hierarchical plan structure, and make inferences at different levels of abstraction in the plan hierarchy. We introduced the Abstract Hidden Markov Model (AHMM), a novel type of stochastic process, provided its dynamic Bayesian network (DBN) structure, and analysed the properties of this network.