1. CSE 574: Artificial Intelligence II Statistical Relational Learning Instructor: Pedro Domingos

2. Logistics Instructor: Pedro Domingos Email: pedrod@cs.washington.edu Office hours: Wednesdays 3:00-3:50, CSE 648 TA: Aniruddh Nath Email: nath@cs.washington.edu Office hours: Mondays 3:00-3:50, CSE 216 Web: http://www.cs.washington.edu/574 Mailing list: cse574a_au11@uw.edu

3. Source Materials Textbook: P. Domingos & D. Lowd, Markov Logic: An Interface Layer for AI, Morgan & Claypool, 2009 Papers Software: Alchemy, etc. Models, datasets, etc.: Alchemy Web site (alchemy.cs.washington.edu)

4. Evaluation Seminar (Pass/Fail) Project (100% of grade) Proposals due: October 19 Progress report due: November 16 Presentation in class Final report due: December 7 Conference submission: Winter 2012 (!)

5. Possible Projects Apply SRL to problem you’re interested in Develop new SRL algorithm Other

6. What Is Statistical Relational Learning? A unified approach to AI/ML Combines first-order logic and probabilistic models Example: Markov logic Syntax: Weighted first-order formulas Semantics: Templates for Markov nets Inference: Logical and probabilistic Learning: Statistical and ILP

7. Why Take this Class? Powerful set of conceptual tools New way to look at AI/ML Powerful set of software tools* Increase your productivity Attempt more ambitious applications Powerful platform for developing new learning and inference algorithms Many fascinating research problems * Caveat: Not mature!

8. Sample Applications Information extraction Entity resolution Link prediction Collective classification Web mining Natural language processing Computational biology Social network analysis Robot mapping Activity recognition Personal assistants Probabilistic KBs Etc.

9. Overview of the Class Background Representation Inference Learning Extensions Applications Your projects

10. Background Markov networks Representation Inference Learning First-order logic Representation Inference Learning (a.k.a. inductive logic programming)

11. Representation “Alphabet soup” Markov logic Properties Relation to first-order logic and statistical models

12. Inference Basic MAP and conditional inference The MC-SAT algorithm Knowledge-based model construction Lazy inference Lifted belief propagation Probabilistic theorem proving

13. Learning Weight learning Generative Discriminative Incomplete data Structure learning and theory revision Statistical predicate invention Transfer learning

14. Extensions Continuous domains Infinite domains Recursive MLNs Relational decision theory

15. Applications (Sampled according to your interests)

16. Your Projects (TBA)

17. Class begins here.

18. AI: The First 100 Years IQ Human Intelligence Artificial Intelligence 195620562006

19. AI: The First 100 Years IQ Human Intelligence Artificial Intelligence 195620562006

20. AI: The First 100 Years IQ Human Intelligence Artificial Intelligence 195620562006

21. The Interface Layer Interface Layer Applications Infrastructure

22. Networking Interface Layer Applications Infrastructure Internet Routers Protocols WWW Email

23. Databases Interface Layer Applications Infrastructure Relational Model Query Optimization Transaction Management ERP OLTP CRM

24. Programming Systems Interface Layer Applications Infrastructure High-Level Languages Compilers Code Optimizers Programming

25. Hardware Interface Layer Applications Infrastructure VLSI Design VLSI modules Computer-Aided Chip Design

26. Architecture Interface Layer Applications Infrastructure Microprocessors Buses ALUs Operating Systems Compilers

27. Operating Systems Interface Layer Applications Infrastructure Virtual Machines Hardware Software

28. Human-Computer Interaction Interface Layer Applications Infrastructure Graphical User Interfaces Widget Toolkits Productivity Suites

29. Artificial Intelligence Interface Layer Applications Infrastructure Representation Learning Inference NLP Planning Multi-Agent Systems Vision Robotics

30. Artificial Intelligence Interface Layer Applications Infrastructure Representation Learning Inference NLP Planning Multi-Agent Systems Vision Robotics First-Order Logic?

31. Artificial Intelligence Interface Layer Applications Infrastructure Representation Learning Inference NLP Planning Multi-Agent Systems Vision Robotics Graphical Models?

32. Logical and Statistical AI FieldLogical approach Statistical approach Knowledge representation First-order logicGraphical models Automated reasoning Satisfiability testing Markov chain Monte Carlo Machine learningInductive logic programming Neural networks PlanningClassical planning Markov decision processes Natural language processing Definite clause grammars Prob. context- free grammars

33. We Need to Unify the Two The real world is complex and uncertain Logic handles complexity Probability handles uncertainty

34. Artificial Intelligence Interface Layer Applications Infrastructure Representation Learning Inference NLP Planning Multi-Agent Systems Vision Robotics Statistical Relational Learning