In this talk I will give an overview of some recent work with colleagues at Stanford and Berkeley on scaling (PO)MDP methods to solve larger problems. The use of approximate probability distributions plays an important role in each of these methods. In some cases it plays a supporting role, while in others it offers a fairly radical departure from traditional methods. I will present some basic theoretical results and some promising preliminary simulation results showing the efficacy of these methods. These results suggest that density estimation may become as important as value function approximation in the search for general and powerful methods for (PO)MDPs.

This talk will describe joint work with Daphne Koller, Andrew Ng, and Andres Rodriguez.

Kee-Eung Kim Last modified: Mon Oct 18 14:37:49 EDT 1999