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Overview

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

We will cover three main topics: probabilistic graphical models (models of belief), decision making (utility, rewards, planning, reinforcement learning), and relational models (entities and relations, knowledge graphs, making predictions based on relational data).

This course builds on the foundations of 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.

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.

Topics

The topics we will cover include semantics, inference and learning for many representations with the goal of understanding how they can be combined. Some subset of following will be covered, depending on the interests of the participants.

Assessment

The course assessment will be based on in-class presentations/participation, peer reviewing and three assignments/projects. The current plan is for participants to contribute to the online textbook on UBC Wiki. Each wiki page will have (at least) one principal author. Each assignment of for you to:

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


Last updated: 2022-09-06, David Poole