Beyond Classical Planning: Procedural Control Knowledge and Preferences in State-of-the-Art Planners

Jorge A. Baier, Christian Fritz, Sheila A. McIlraith

Real-world planning problems can require search over thousands of actions and may yield a multitude of plans of differing quality. To solve such real-world planning problems, we need to exploit domain control knowledge that will prune the search space to a manageable size. And to ensure that the plans we generate are of high quality, we need to guide search towards generating plans in accordance with user preferences. Unfortunately, most state-of-the-art planners cannot exploit control knowledge, and most of those that can exploit user preferences require those preferences to only talk about the final state. Here, we report on a body of work that extends classical planning to incorporate procedural control knowledge and rich, temporally extended user preferences into the specification of the planning problem. Then to address the ensuing nonclassical planning problem, we propose a broadly-applicable \emph{compilation technique} that enables a diversity of state-of-the-art planners to generate such plans without additional machinery. While our work is firmly rooted in AI planning it has broad applicability to a variety of computer science problems relating to dynamical systems.

Subjects: 1.11 Planning; 11. Knowledge Representation

Submitted: Apr 16, 2008


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