CS539: Probabilistic Agents
This course will study how to construct an intelligent agent based on
probabilistic reasoning. We will begin by studying reinforcement
learning including both Markov decision problems (MDPs) and
partially-observable Markov decision problems (POMDPs). Then we will
consider several reinforcement learning algorithms including
prioritized sweeping, TD(lambda), Q learning, and direct policy
search. The need to understand probabilistic models of POMDPs will
lead us to study belief networks (bayesian networks), algorithms for
reasoning in belief networks, and special kinds of networks,
particularly Hidden Markov Models (HMMs). We will study the
forward-backward algorithm for reasoning in HMMs as well as Monte
Carlo methods. To learn HMMs, we will first study the EM algorithm
for simple, "naive bayes" networks. Then we will apply EM to learn
HMMs. After taking some time to study applications of HMMs in
biology, speech recognition, and robotics, we will combine HMMs with
reinforcement learning to construct complete probabilistic agents.
The coursework will consist primarily of reading and programming
assignments along with midterm and final exams.
Prerequisites: CS530 or consent of the instructor; basic knowledge of
Registration Information: 4 Units. MWF 9:00-9:50 Rogers 332 CRN 25321.
Viewgraphs for Lectures
Tom Dietterich, firstname.lastname@example.org