Generalizing and Executing Plans
Our work addresses the problem of generalizing a plan and representing it for efficient execution. A key area of automated planning is the study of how to generate a plan for an agent to execute. The plan itself may take on many forms: a sequence of actions, a partial ordering over a set of actions, or a procedure-like description of what the agent should do. Once a plan is found, the question remains as to how the agent should execute the plan. For simple forms of representation (e.g., a sequence of actions), the answer to this question is straightforward. However, when the plan representation is more expressive (e.g., a GOLOG program ), or the agent is acting in an uncertain world, execution can be considerably more challenging. We focus on the problem of how to generalize various plan representations into a form that an agent can use for efficient and robust online execution.
Srivistava et al. propose a definition of a generalized plan as an algorithm that maps problem instances to a sequence of actions that solves the instance . Our work fits nicely into this formalism, and in Section 3 we describe how a problem (i.e., a state of the world and goal) is mapped to a sequence of actions (i.e., what the agent should do).
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