Planning is essential for an action-oriented, goal-driven software agent. In order to achieve a specific goal, an agent must first generate a plan. However, as the poet Robert Burns once noted, the best laid plans can often go awry. Each step of the plan is subject to the possibility of failure, a truth particularly relevant in the realworld or a realistic simulated environment. External influences not originally considered can often cause sudden, unanticipated consequences during the execution of the plan. When this happens, an intelligent software agent needs to answer the following important questions: What are the consequences of this event on its plan? How will the plan be affected? Can the plan be adjusted to accommodate the unanticipated effects? The research described in this thesis develops a model whereby intelligent agents can automatically determine consequences of unplanned events. Such a model provides agents with the ability to detect if and how events will affect the plan. This allows agents to subsequently modify the plan to mitigate unfavorable consequences or take advantage of favorable consequences.