CPSC 522 - Artificial Intelligence 2
Readings

Readings

These readings will be updated throughout the term. 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).

Weekly paper presentations

Each week a group of students will make a coordinated presentation of one or more papers for approximately 30 minutes. Each student will do two presentations. Here is the schedule (subject to change):

• Jan 11. Vibhav Gogate and Rina Dechter, SampleSearch: A Scheme that Searches for Consistent Samples, In 11th International Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
• Jan 18. 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.
• Jan 23. Judea Pearl, Causality, See Turing award lecture or UBC-CS DLS (which is similar but less rushed). Read J. Pearl, "Causal inference in statistics: An overview," Statistics Surveys, 3:96--146, 2009, up to and including Section 3.2.
• Feb 1. Sammut, C. A. (1988). Logic Programs as a Basis for Machine Learning. In P. Brazdil (Ed.), Proceedings of the International Workshop on Logic, Meta-Reasoning and Learning, Sesimbra, Portugal and/or Sammut, C. A. (1993). The Origins of Inductive Logic Programming. In S. Muggleton (Ed.), Third International Workshop on Inductive Logic Programming, Bled, Solvenia.
• Feb 8. Barry Smith, Ontology, in L. Floridi (ed.), Blackwell Guide to the Philosophy of Computing and Information, 2003 and/or John Sowa, Future Directions for Semantic Systems, Intelligence-based Software Engineering, 2011.
• Feb 15. - Koren, Y., Bell, R. and Volinsky, C., Matrix Factorization Techniques for Recommender Systems, IEEE Computer 2009.
• Feb 20 - midterm break
• Feb 27. - Markov Logic Networks Matthew Richardson and Pedro Domingos
• Mar 6. - Blei, Ng, and Jordan, "Latent Dirichlet Allocation", JAIR 3 (2003)
• Mar 13. - Darius Braziunas and Craig Boutilier, Elicitation of Factored Utilities, AI Magazine 29(4):79--92, Winter (2008).
• Mar 22. - Action Selection for MDPs: Anytime AO* vs. UCT, Blai Bonet and Hector Geffner. Proc. 26th AAAI Conf. on Artificial Intelligence (AAAI). Toronto, Canada. 2012. Pages 1749-1755.
• Mar 27. - Cognitive Robotics Levesque, H. and Lakemeyer, G., Handbook of Knowledge Representation, Elsevier, 2008. (29 is holiday)
• Apr 3 - Reinforcement Learning - RALP [Parr 2010] or Pazis & Parr 2011

Books

Much of the basics is covered in

• 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:

• AIJ Artificial Intelligence Journal.
• JAIR. Journal of AI Research.
• JMLR. Journal of Machine Learning Research.
• IJCAI,
• AAAI,
• UAI,
• KR,
• NIPS.
• Citations and repositories: Citeseer, The Computing Research Repository

Ontologies

• Basic Formal Ontology (BFO)
• OWL, I'd also recommend OWL 2 Primer and browsing Structural Specification and Functional-Style Syntax for the syntax and Direct Semantics.
• OBO foundry, One Geology, data.gov.

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

• K. Kersting, S. Natarajan, D. Poole, Statistical Relational AI: Logic, Probability and Computation Technical Report 2012.

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

• J. Pearl, Causal inference in statistics: An overview Statistics Surveys, 3:96--146, 2009.

Last updated: 2013-01-01, David Poole