General Information

When: Mondays and Wednesdays, 1:00-2:30pm.

What: One of the primary goals of AI is the design, control and analysis of agents or systems that behave appropriately in various circumstances. Such intelligent agents require the ability to decide how to act as circumstances vary. In turn, good decision making requires that the agent have knowledge or beliefs about its environment and its dynamics, about its own abilities to observe and change the environment, and about its own goals and preferences. In this course we will examine some of the techniques for modeling decision problems of various types and the computational methods used to solve them. We will focus mainly on probabilistic models of reasoning, and on sequential decision making.
The course is intended for advanced undergraduate students and for graduate students, and will provide an introduction to the on-going research in the field of reasoning under uncertainty, which has been very active during the last decade.
The course will cover both the theoretical basis of decision making under uncertainty, and the practical applications of these algorithms. We will cover the following topics:

• The principles of Bayesian inference
• Belief networks
  • Syntax and semantics
  • Exact and approximate inference
  • Learning methods
• Temporal models
  • Hidden Markov Models
  • Dynamic Bayes nets
  • Kalman filters
• Basics of utility theory
• Markov Decision Processes
  • Dynamic programming methods
  • Monte Carlo methods
  • Temporal-difference learning algorithms
  • Generalization and function approximation
  • Structured state and action spaces
• Partially Observable Markov Decision Processes
  • Exact methods
  • Approximate methods

Instructor

Office: McConnell 326

Office Hours: