Probabilistic and decision-theoretic planning, which operates under conditions of uncertainty, has important applications in science and engineering, but such stochastic methods have been under-utilized because these planners do not scale to large, complicated problems.

In contrast, the past decade has seen tremendous scale-up in deterministic planning techniques, which do not directly address the same challenges of uncertainty head on. This project provides a systematic framework for exploiting this progress in deterministic planning technology - be it classical, temporal or partial satisfaction planning problems - in stochastic planning as well, by novel adaptation of theory and/or methods of determinization, hindsight optimization, and machine learning.

This project is helping to bridge the traditional divide between deterministic and stochastic planning communities, and it is bringing stochastic planning technology to real-world applications.