Planning Using a Portfolio of Reduced Models Extended Abstract

Existing reduced model techniques simplify a problem by applying a uniform principle to reduce the number of considered outcomes for all state-action pairs. It is non-trivial to identify which outcome selection principle will work well across all problem instances in a domain. We aim to create reduced models that yield near-optimal solutions, without compromising the run time gains of using a reduced model. First, we introduce planning using a portfolio of reduced models, a framework that provides flexibility in the reduced model formulation by using a portfolio of outcome selection principles. Second, we propose planning using cost adjustment, a technique that improves the solution quality by accounting for the outcomes ignored in the reduced model. Empirical evaluation of these techniques confirm their effectiveness in several domains.