CPSC 532 - Topics in AI:
Statistical Relational Artificial Intelligence
Spring 2017
Readings
Where possible, I
have tried to find sources that are freely available. Some of them are
only free using a university computer (e.g. using the VPN). Some of
these are just names that you can google for. (For these Google seems
to give a good coverage). Some are more specific (when Google doesn't
seem to find good resources).
Discussion Papers
- Ghahramani, Z. (2015). Probabilistic
machine learning and artificial intelligence. Nature, 521(7553):
452-459. http://dx.doi.org/10.1038/nature14541
- Section 3.2 of Textbook.
- Blei, Ng, and Jordan, "Latent Dirichlet Allocation", JAIR 3
(2003)
- J. Pearl, Causal inference in statistics: An overview
Statistics Surveys, 3:96--146, 2009.
- Vibhav Gogate and Rina Dechter, SampleSearch: A Scheme that Searches for Consistent Samples, In 11th International Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
- Using Semantics & Statistics to Turn Data into Knowledge. Jay
Pujara, Hui Miao, Lise Getoor, William W. Cohen. AI Magazine 36.1
(2015). (pdf)
- Daniel Lowd. Closed-Form Learning of Markov Networks from Dependency Networks. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-12), 2012. Catalina Island, CA.
- I. Shpitser, K. Mohan, and J. Pearl, "Missing data as a causal and probabilistic problem"
UCLA Cognitive Systems Laboratory, Technical Report (R-454), July 2015.
In Marina Meila and Tom Heskes (Eds.), Proceedings of the 31st
Conference on Uncertainty in Artificial Intelligence:
802--811, 2015. (or another paper from http://bayes.cs.ucla.edu/csl_papers.html)
an
application in computational sustainability
Books
Much of the basics is covered in
-
Luc De Raedt,
Kristian Kersting,
Sriraam Natarajan, and
David Poole
Statistical
Relational Artificial Intelligence: Logic, Probability, and
Computation Morgan and Claypool, 2016. Full text is available
from UBC (you may have to use a VPN if off-campus).
- D. Poole and A. Mackworth Artificial Intelligence: Foundations of
Computational Agents (Cambridge University Press, 2010)
- S. Russell and P. Norvig, Artificial Intelligence : A Modern
Approach, 3rd edn (Prentice-Hall, 2010)
- D. Koller and N. Friedman. Probabilistic Graphical Models:
Principles and Techniques. (MIT Press 2009)
- A. Darwiche, Modeling and Reasoning with Bayesian Networks
(Cambridge University Press, 2010)
- De Raedt, L.; Frasconi, P.; Kersting, K.; Muggleton,
S.H. (Eds.), Probabilistic Inductive Logic Programming
Springer, 2008
- S. Thrun, W. Burgard and D. Fox, Probabilistic Robotics, (MIT Press
2006)
- Martijn van Otterlo, The Logic of Adaptive Behavior -
Knowledge Representation and Algorithms for Adaptive Sequential
Decision Making under Uncertainty in First-Order and Relational
Domains, (IOS Press, 2009).
- Luc De Raedt.
Logical and Relational Learning.
Springer.2008.
Journals and Conferences
The major journals and conferences related to this course are:
Ontologies
Philosophy and Practice of Science
There are lot of
books about science, pseudoscience and non-science --- this is very
relevant to the course as science is one of the best-developed mechanisms for
discovering what is true in the world. See, e.g., The Scientific
Method Made Easy.
Probabilistic Relational Models
Decision-theoretic Planning and Reinforcement Learning
- Boutilier, Dean
and Hanks ``Decision Theoretic Planning: Structural Assumptions and
Computational Leverage'', JAIR, Vol 11, 1--94, 1999
- Kaelbling, L.P., Littman, M.L., and Moore, A.W. (1996) "Reinforcement
Learning: A Survey", JAIR,
Volume 4, pages 237-285.
- Csaba Szepesvari, Algorithms
for Reinforcement Learning, Morgan & Claypool, 2010.
Causality
Last updated: 2016-12-29, David Poole