This is an advanced AI course on mixing uncertainty and relational models: what should an agent do based on its ability, its beliefs, its perceptions and its values/goals in an uncertain environment that consists of individuals (things, objects) with relationships among them. For example, in a medical diagnosis system, we want to make a probabilistic prediction of the effect of a treatment on a patient, conditioned on the patient's electronic health record (history of doctor's visits, tests, treatments, fitness data, etc). In geology, we might condition on the description of a geological area including sensing data to predict earthquakes,


We will assume the following background (which you can get from courses, MOOCs, books):

Structure of the Course

Instructor: David Poole, 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, 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:

Grades will be on Blackboard Connect. Discussion is on Piazza.


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


The following is a good for the background, but we will go into more detail in some parts and less in others:

For background reading, see CPCS 522 Wiki from 2016, which contains background material.


The course assessment will be based on assignments, presentations, reviewing, and projects. The participants will use the UBC Wiki as a collaborative research platform.

Last updated: 2017-01-03, David Poole