Generalizing and Executing Plans

We address the problem of generalizing a plan to maximize To date we have focused on generalizing partial-order plans the flexibility and robustness with which an agent can execute (POPs). We described how to generalize a POP for online it. A key area of automated planning is the study of how execution, and laid the groundwork for the approach our research to generate a plan for an agent to execute. The plan itself will follow (Muise, McIlraith, and Beck 2011a). We may take on many forms: a sequence of actions, a partial ordering also presented a method to optimally relax a sequential plan over a set of actions, or a procedure-like description to a POP (Muise, Mcilraith, and Beck 2011b), providing a of what the agent should do.