Creating automated plans for Semantic Web applications through planning as model checking

Abstract

The uncertainties of planning engendered by nondeterminism and partial observability have led to a melding of model checking and artificial intelligence. The result is planning as model checking. Because planning as model checking tests sets of states and sets of transitions at once, rather than single states, the method remains robust and viable in domains of large state spaces and varying levels of uncertainty. We develop a test bench for Semantic Web agents and use model-based planning to derive strong plans, strong cyclic plans, and weak plans. Our results suggest potential robustness and efficacy in devising plans for agent actions in the Semantic Web environment.