CPSC 522 - Artificial Intelligence 2
Schedule
January-April 2019
Here is a tentative schedule. Which students are presenting on
each day: http://wiki.ubc.ca/Course:CPSC522/StudentPresentations2019.
- Jan 2 - AI and Agents. Slides: lect1.pdf, lect2.pdf. Readings:
AIFCA 2e: chapter 1.
- Jan 8 - agents, AIpython (agent.py was demoed),
probability, independence and
belief networks. Readings:
AIFCA 2e: Section 2.1-2.2, 8.1-8.3.
- Jan 9 - independence and
representing CPDs , inference. Belief and Decision networks
applet (go to downloads).
Discussion papers (all): Artificial
Intelligence - The Revolution Hasn't Happened Yet, Human-level
intelligence or animal-like abilities? (and a video at ucla). (These should both be
free downloads from UBC).
- Jan 15 - Markov
models. First part of
Lifted
inference.pdf.
Student presentations:
-
Mark Chavira and Adnan
Darwiche, On probabilistic inference by weighted model counting,
Artificial Intelligence,
Volume 172, Issues 6-7, April 2008
-
Probabilistic inference in hybrid domains by weighted model integration.
V Belle, A Passerini, G Van den Broeck. Proceedings IJCAI 2015
- Jan 16 - Stochastic
Simulation. Localization demo: Localization
demo (you might need to download it).
- Jan 22 - Student presentations:
-
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots.
D. Fox, Burgard, W., Dellaert, F., Thrun, S., AAAI, 1999
-
FastSLAM: A factored solution to the simultaneous localization and mapping problem
M Montemerlo, S Thrun, D Koller, B Wegbreit - Aaai/iaai, 2002
- Jan 29 - Class cancelled
- Feb 5 - Learning and Causality. Student presentations:
- Chickering, D. M. and Meek, C. (2002). "Finding optimal Bayesian networks". In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pages 94-102. Morgan Kaufmann.
- Yitao Liang, Jessa Bekker, and Guy Van den Broeck. "Learning the
Structure of Probabilistic Sentential Decision Diagrams". In:
Conference on Uncertainty in Artificial Intelligence (UAI). 2017.
- Feb 6 - Utility.
- Feb 12 - Student presentations:
-
Bareinboim, E., and Pearl, J. 2016. Causal inference and the
data-fusion problem. Proceedings of the National Academy of Sciences
113(27):7345-7352.
-
Shpitser, I., K. Mohan, and J. Pearl (2015). Missing data as a causal and probabilistic
problem. In
Proceedings of the Thirty-First Conference on Uncertainty in Artificial
Intelligence
- Feb 13 - Making
decisions. See AIspace Belief and Decision networks
applet (go to downloads).
- Feb 19 - Midterm break
- Feb 26 - decision processes. Student presentations:
-
Simultaneous Elicitation of Preference Features and Utility.
Craig Boutilier, Kevin Regan and Paolo Viappiani.
Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence (AAAI-10) , pp.1160--1167, Atlanta GA (2010).
-
Adapting a Kidney Exchange Algorithm to Align with Human Values.
Rachel Freedman,
Jana Schaich Borg, Walter Sinnott-Armstrong
John P. Dickerson
Vincent Conitzer, Proc. AAAI-2018.
- Feb 27 - decision processes; Value Iteration Applet.
reinforcement
learning. Tiny
game .
- Mar 5 - reinforcement
learning. See AIPython
reinforcement learning code or RL code.
- Mar 12. Discussion paper:
- Mar 19: Discussion paper:
- Mar 20 - Regularization, Pseudocounts and Probabilistic Mixtures,
MLNs.pptx, relational-reps.pdf
- Mar 26: Discussion paper: Sections I-VII of
-
Nickel, M.; Murphy, K.; Tresp, V.; and Gabrilovich, E. 2016. A review of relational machine learning for knowledge graphs. Proceedings of the IEEE 104(1):11-33.
- Mar 27 -
Reln_prob_models.pdf
- Apr 1 - Discussion papers
-
Guy Van den Broeck and Adnan Darwiche. On the complexity and
approximation of binary evidence in lifted inference, In Advances in
Neural Information Processing Systems 26 (NIPS), 2013
-
Jennifer Neville, David Jensen; Relational Dependency Networks
Relational Dependency Networks, Journal of Machine Learning
Research, 8:653--692, 2007.
- Apr 2 - Semantic_science.pdf
Last updated: 2019-02-06, David Poole