In this paper, a novel probabilistic approach to intention recognition for partial-order plans is proposed. The key idea is to exploit independences between subplans to substantially reduce the state space sizes in the compiled Dynamic Bayesian Networks. This makes inference more efficient. The main con- tributions are the computationally exploitable definition of subplan structures, the introduction of a novel Lay- ered Intention Model and a Dynamic Bayesian Net- work representation with an inference mechanism that exploits consecutive and concurrent subplans' indepen- dences. The presented approach reduces the state space to the order of the most complex subplan and requires only minor changes in the standard inference mecha- nism. The practicability of this approach is demon- strated by recognizing the process of shelf-assembly.