12:00, 20 Mar 1996, WeH 7220 Algorithms for Sequential Decision Making Michael L. Littman Brown University Sequential decision making is a fundamental task faced by any intelligent agent in an extended interaction with its environment. Recently, I examined a collection of computational problems arising in the study of sequential decision making under uncertainty; one problem is finding optimal behavior for partially observable Markov decision processes (POMDPs), a type of sequential problem in which the state of the system and its future evolution are modeled stochastically. I will describe an algorithm I developed for solving POMDPs exactly, and show how it compares favorably to existing algorithms for this problem. I will also present some preliminary results on the use of a reinforcement-learning approach to solve larger problem instances approximately; this technique has been used successfully to find good policies for a robot navigating in an office environment.