This is an advanced AI course on what should an agent do based on its ability, its beliefs, its perceptions and its values/goals in an uncertain environment. We will assume basic logic, probability, and search, such as covered in an introductory AI course.

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 not only the ability to act but also 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 its goals and preferences.

This course builds on the foundations of logic, dynamic systems, decision theory, knowledge representation and machine learning all of which will be presented in enough detail to understand the rest of the course.

We will begin by studying representations of belief and reasoning methods. In the first half of the couce, we will cover enough to understand current research in statistical relational AI, which is about probabilistic knowledge about things and relations amongst things. In the second half of the course, we will cover decision-theoretic planning and reinforcement learning which are about how to act in dynamic stochastic environments.

Design Space of Intelligent Agents Covered

This course will cover the design space of intelligent computational agents, so that you con understand the frontier of research. We cover the following dimensions of the design space:

Unfortunately these cannot be studied independently, as they interact in complex ways.

Structure of the Course

Instructor: David Poole, Office hours: Wednesdays 11:30-12:20, Fridays 2:00-2:30 or by appointment (room ICCS 127).

There will be 3 hours of in-class interaction per week. The classes will be a mix of lectures on the foundations, student presentations, discussion of research papers and problem solving. This is a participatory class; everyone will be expected to participate fully, to have read the reading material before class, and come ready to discuss and critically analyze it.

The classes will be held:

Discussions and grades are on Blackboard Vista.


Everyone will be expected to choose a "vertical domain", with 4 properties: (a) you know something about it, (b) someone may be interested in predictions in this domain, (c) there is potentially rich data about the domain and (d) there are complex interactions about the concepts in the domain. Each participant needs to be ready to explain how the concepts covered in class can apply to their domain.


The course assessment will be based on assignments, in-class participation, a midterm exam, and a final research paper. The evaluation of the final project will be based on peer review. Assignments will not be traditional assignments, but will be to apply what is being covered in class to your vertical domain, and to present it to the class.

Participants will be expected to complete a small course project (details to be announced). The project will be decided in consultation with the instructor and may consist of an implementation of a particular reasoning, decision-making or planning theory (or theories), a short research paper on problem related to a course topic, or a critical literature survey. You are encouraged to collaborate with other students to find complementary synergistic projects (e.g., sharing the same code to test different hypotheses), but your written project must be the your own. You will be required to hand in a short proposal in the middle of the term, and then 4 copies of your project in the last week of classes. You will be expected to peer review three other projects and give a conference-style presentation of your project. You will have a chance to revise your project based on the reviews and feedback from your presentation.

The breakdown of marks is tentatively:

Last updated: 2011-12-15, David Poole