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10-803 Markov Logic Networks

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

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10-803 Markov Logic Networks

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  1. 10-803Markov 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. 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. Sample Applications

  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 1956 2006 2056

  17. AI: The First 100 Years IQ Human Intelligence Artificial Intelligence 1956 2006 2056

  18. AI: The First 100 Years Artificial Intelligence IQ Human Intelligence 1956 2006 2056

  19. The Interface Layer Applications Interface Layer Infrastructure

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

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

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

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

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

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

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

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

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

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

  30. Logical and Statistical AI

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

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

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