1. 10-803 Markov Logic Networks Instructor: Pedro Domingos

2. Logistics Instructor: Pedro Domingos Email: pedrod@cs.washington.edu Office: Wean 5317 Office hours: Thursdays 2:00-3:00 Course secretary: Sharon Cavlovich Web: http://www.cs.washington.edu/homes/ pedrod/803/ Mailing list: 10803-students@cs.cmu.edu

3. Source Materials Textbook: P. Domingos & D. Lowd, Markov Logic: An Interface Layer for AI, Morgan & Claypool, 2008 Papers Software: Alchemy (alchemy.cs.washington.edu) MLNs, datasets, etc.: Alchemy Web site

4. Project Possible projects: Apply MLNs to problem you’re interested in Develop new MLN algorithms Other Key dates/deliverables: This week: Download Alchemy and start playing October 9 (preferably earlier): Project proposal November 6: Progress report December 4: Final report and short presentation Winter 2009: Conference submission (!)

5. What Is Markov Logic? A unified language for AI/ML Special cases: First-order logic Probabilistic models Syntax: Weighted first-order formulas Semantics: Templates for Markov nets Inference: Logical and probabilistic Learning: Statistical and ILP

6. 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!

7. 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.

8. Overview of the Class Background Markov logic Inference Learning Extensions Your projects

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

10. Markov Logic Representation Properties Relation to first-order logic and statistical models Related approaches

11. Inference Basic MAP and conditional inference The MC-SAT algorithm Knowledge-based model construction Lazy inference Lifted inference

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

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

14. Your Projects (TBA)

15. Class begins here.

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

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

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

19. The Interface Layer Interface Layer Applications Infrastructure

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

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

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

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

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

25. Operating Systems Interface Layer Applications Infrastructure Virtual machines Hardware Software

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

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

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

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

30. 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

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

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