Task-Decomposition via Plan Parsing

Task-decomposition planners make use of schemata that de ne tasks in terms of partially ordered sets of tasks and primitive actions. Most existing taskdecomposition planners synthesize plans via a topdown approach, called task reduction, which uses schemata to replace tasks with networks of tasks and actions until only actions remain. In this paper we present a bottom-up plan parsing approach to task-decomposition. Instead of reducing tasks into actions, we use an incremental parsing algorithm to recognize which partial primitive plans match the schemata. In essence, our approach exploits the observation that schemata are a convenient means for reducing search. We compile the schemata into a declarative search control language (like that used in machine learning research), which rejects plan re nements that cannot be parsed. We demonstrate that neither parsing nor reduction dominates the other on e ciency grounds and provide preliminary empirical results comparing the two. We note that our parsing approach allows convenient comparison (and combination) of di erent search control technologies, generates minimal plans, and handles expressive languages (e.g., universal quanti cation and conditional e ects) with ease.