In most A.I. planning approaches it is assumed that the planning agent has complete knowledge about its environment. If this is the case the agent acts in two steps: It first plans and then executes its plan. However, humans do usually not behave this simple, as in most real world problems knowledge about the environment is incomplete. To solve real world problems, acting, sensing and planning has to be interleaved in a cyclic manner: Knowledge has to be acquired during plan execution and the plan has to be adopted incrementally to the acquired knowledge. In our work, we employ the Discrete Event Calculus Knowledge Theory (DECKT) and combine it with a Lazy Branching strategy to interleave planning and plan execution for problems with incomplete knowledge: We make optimistic assumptions for unknown contingencies and lazily postpone the planning for a certain contingency until it is resolved. As the Event Calculus accounts for time, the proposed approach allows to combine planning with incomplete knowledge, concurrency and explicit time.