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Humans have always reasoned about complex problems by organizing them into hierarchical structures. One approach to artificial intelligence planning is to design intelligent agents capable of breaking complex problems into multiple levels of abstraction so that at any one level, the problem becomes small and simple. However, for an agent to reason at multiple levels of abstraction, it needs knowledge at those abstraction levels. Hierarchical Task Network (HTN) planning allows us to do precisely that. This thesis presents a novel HTN planning algorithm that uses iterative tree traversal to refine HTNs. We also develop a purely reactive HTN acting algorithm using a similar procedure. Preserving the hierarchy in HTN plans can be helpful during execution. We make use of this fact to develop an algorithm for integrated HTN planning and acting. We show through experiments that our algorithm is an improvement over a widely used approach to planning and control.