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).

Your February assignment will need to cover a very specific research topic. Your page will need to refer to at least two research papers (by disjoint sets of authors) and explain how one of the papers is an advance on the other. This should be a different pair that one presented in class. Here is a random selection of papers:

- Vibhav Gogate and Rina Dechter, SampleSearch: A Scheme that Searches for Consistent Samples, In 11th International Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
- 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)
- Sammut, C. A. (1988). Logic Programs as a Basis for Machine Learning. and/or Sammut, C. A. (1993). The Origins of Inductive Logic Programming. from Sammut's pages on ILP
- Robot Scientist, e.g., https://dx.doi.org/10.1109%2FMC.2009.270 or http://science.sciencemag.org/content/324/5923/85 or http://rsif.royalsocietypublishing.org/content/12/104/20141289
- Restricted Boltzmann machines
- Rao Blackwellized particle filter
- 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. Or another paper on ontologies, such as at http://ontology.buffalo.edu/smith/ or http://www.jfsowa.com/pubs/index.htm. or
- Koren, Y., Bell, R. and Volinsky, C., Matrix Factorization Techniques for Recommender Systems, IEEE Computer 2009.
- Markov Logic Networks Matthew Richardson and Pedro Domingos
- Problog, theory, implementation or applications
- Blog and open-universe models (search google for "Blog and open-universe models")
- Record Linkage and identity uncertainty
- Blei, Ng, and Jordan, "Latent Dirichlet Allocation", JAIR 3 (2003)
- Darius Braziunas and Craig Boutilier, Elicitation of Factored Utilities, AI Magazine 29(4):79--92, Winter (2008).
- Interactive preference eliciition an application in computational sustainability and a tool
- Matheson, J.E. (1990). Using influence diagrams to value information and control. In R.M. Oliver and J.Q. Smith (Eds.), Influence Diagrams, Belief Nets and Decision Analysis, chapter 1, pp. 25-48. Wiley.
- 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.
- Cognitive Robotics Levesque, H. and Lakemeyer, G., Handbook of Knowledge Representation, Elsevier, 2008.
- Reinforcement Learning - RALP [Ron Parr 2010] or Pazis & Parr 2011
- Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518: 529-533. or Silver, D., et al (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587): 484-489. See also Samuel, A.L. (1959). Some studies in machine learning using the game of checkers. IBM Journal on Research and Development, 3(3): 210–229.

Much of the basics is covered in

- D. Poole and A. Mackworth Artificial Intelligence: Foundations of Computational Agents (Cambridge University Press, 2nd edition, 2017)
- 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.

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

- 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.

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.

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

- 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.

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