In the context of modeling and reasoning about agent actions, contingent and classical planning can often be respectively seen as adopting "extreme pessimism" and "extreme optimism" about the action outcomes. For many everyday scenarios of human reasoning (and thus for many types of autonomous systems), both these approaches are just too extreme. Following Jensen, Veloso, and Bryant (2004), we examine a planning model that interpolates between classical and contingent planning via tolerance to arbitrary k faults occurring during plan execution. We show that an important fragment of this fault tolerant planning (FT-planning) exhibits both an appealing solution structure, as well as appealing worst-case time-complexity properties. We also show that such FT-planning tasks can be efficiently compiled into classical planning as long as the number of possible faults per operator is bounded by a constant, and we show that this compilation can be attractive in practice.
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