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Overview

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, which are updated through perception, about its own abilities, 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, preference and actions as well as how to reason and learn such representations. The aim is to understand techniques that work in complex stochastic partially-observable environments that include multiple objects and other agents. We concentrate on methods that have the potential to work in such environments (even though there are few exiting representations that do everything).

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, poole@cs.ubc.ca. Office hours: After class any day or by appointment (room ICCS 109).

There will be 3 hours of in-class interaction per week. The classes will be a mix of lectures on the foundations and student presentations and discussion of research papers. 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 Canvas.

Topics

The topics we will cover include semantics, inference and learning for many representations with the goal of inderstanding how they can be combined.

Assessment

The course assessment will be based on in-class presentations and three assignments/projects. The participants will write a textbook on UBC Wiki. Each wiki page will have one principal author and two secondary authors. Each assignment of for you to:

You can be involved in multiple authorship/critiques, but you get to be evaluated by the (average of the) one(s) you choose to be evaluated for. (You can think of the critique / evaluator like the validation set / test set in machine learning.)

Each month's pages will be different theme:

Each wiki page should include both the prior argument and the evidence that it works.


Last updated: 2012-12-04, David Poole