Identifying executable plans Tania Bedrax-Weiss∗ Jeremy D. Frank Ari K. J´onssony Conor McGann∗ NASA Ames Research Center, MS 269-2 Moffett Field, CA 94035-1000, ftania,frank,jonsson,
[email protected] Abstract AI solutions for planning and plan execution often use declarative models to describe the domain of interest. Generating plans for execution imposes a different set The planning system typically uses an abstract, long- of requirements on the planning process than those im- term model and the execution system typically uses a posed by planning alone. In highly unpredictable ex- ecution environments, a fully-grounded plan may be- concrete, short-term model. In most systems that deal come inconsistent frequently when the world fails to with planning and execution, the language used in the behave as expected. Intelligent execution permits mak- declarative model for planning is different than the lan- ing decisions when the most up-to-date information guage used in the execution model. This approach en- is available, ensuring fewer failures. Planning should forces a rigid separation between the planning model acknowledge the capabilities of the execution system, and the execution model. The execution system and the both to ensure robust execution in the face of uncer- planning system have to agree on the semantics of the tainty, which also relieves the planner of the burden plan, and having two separate models requires the sys- of making premature commitments. We present Plan tem designer to replicate the information contained in Identification Functions (PIFs), which formalize what the planning model in the execution model.