- First class Wednesday January 4th.
- Assignment 0 due on Friday 6 January
- Assignment 1, part A due on Wednesday 11 January

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):

- Basic AI. The idea of representations, inference, learning.
- Probability, including graphical models. We will assume you know what a Bayesian network (belief network) is, and some basic inference algorithms (such as variable elimination and MCMC).
- Machine learning: you need to know some of the basics, e.g., as covered in CPSC 340.
- First-order logic. Preferably some logic programming.

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, 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:

- Mondays and Wednesdays: 12:00-1:30, ICCS 206. The first class will be on Wednesday, January 4, 2017.

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

- Representations
- Combinations of Probabilistic models (directed and undirected models) and First-order logic
- Relational, identity, number and existence uncertainty
- Representations for models and data that semantically interact
- Inference: ground and lifted inference; exact, bounded and approximate inference
- Learning: learning about individuals and general knowledge, learning probabilities, structure learning
- Preferences and utilities. Actions. Relational reinforcement learning

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

- Statistical Relational Artificial Intelligence: Logic, Probability, and Computation Morgan and Claypool, 2016. Full text is available for download from UBC (you may have to use a VPN if off-campus).

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.