A robot acting in the real world must use flexible plans because actions will sonhetimes fail to produce desired effects, and unexpected events will sometimes denhand the robot shift its attention. A plan is usually construed as a list of primitive robot actions to be executed one after another but in a complex donhain, a plan must be structured to cope effectively with the myriad unpredictable details it will encounter during execution. However, adding structure to a plan involves more than augmenting the primitive plan representation; it requires a complete model of interaction with the world called situation-driven execution. Situation-driven execution assumes that a plan consists of tasks with three major components: a satisfaction test, a window of activity, and a set of execution methods that are appropriate in different circumstances. Execution of such a plan proceeds by selecting an unsatisfied task and choosing a method to achieve it based on the current world state. A task may be executed as many times as necessary to keep it satisfied while it is active. This thesis proposes a plan and task representation based on program-like reactive action packages, or RAPs. A plan consists of RAP-defined tasks and each RAP
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