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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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**10-803Markov 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/ • Mailing list: 10803-students@cs.cmu.edu**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**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 (!)**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**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!**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**Overview of the Class**• Background • Markov logic • Inference • Learning • Extensions • Your projects**Background**• Markov networks • Representation • Inference • Learning • First-order logic • Representation • Inference • Learning (a.k.a. inductive logic programming)**Markov Logic**• Representation • Properties • Relation to first-order logic and statistical models • Related approaches**Inference**• Basic MAP and conditional inference • The MC-SAT algorithm • Knowledge-based model construction • Lazy inference • Lifted inference**Learning**• Weight learning • Generative • Discriminative • Incomplete data • Structure learning and theory revision • Statistical predicate invention • Transfer learning**Extensions**• Continuous domains • Infinite domains • Recursive MLNs • Relational decision theory**Your Projects**(TBA)**AI: The First 100 Years**IQ Human Intelligence Artificial Intelligence 1956 2006 2056**AI: The First 100 Years**IQ Human Intelligence Artificial Intelligence 1956 2006 2056**AI: The First 100 Years**Artificial Intelligence IQ Human Intelligence 1956 2006 2056**The Interface Layer**Applications Interface Layer Infrastructure**Networking**WWW Email Applications Internet Interface Layer Protocols Infrastructure Routers**Databases**ERP CRM Applications OLTP Interface Layer Relational Model Transaction Management Infrastructure Query Optimization**Programming Systems**Programming Applications Interface Layer High-Level Languages Compilers Code Optimizers Infrastructure**Hardware**Computer-Aided Chip Design Applications Interface Layer VLSI Design Infrastructure VLSI modules**Architecture**Operating Systems Applications Compilers Interface Layer Microprocessors ALUs Infrastructure Buses**Operating Systems**Applications Software Interface Layer Virtual machines Infrastructure Hardware**Human-Computer Interaction**Applications Productivity Suites Interface Layer Graphical User Interfaces Infrastructure Widget Toolkits**Artificial Intelligence**Planning Robotics Applications NLP Multi-Agent Systems Vision Interface Layer Representation Inference Infrastructure Learning**Artificial Intelligence**Planning Robotics Applications NLP Multi-Agent Systems Vision Interface Layer First-Order Logic? Representation Inference Infrastructure Learning**Artificial Intelligence**Planning Robotics Applications NLP Multi-Agent Systems Vision Interface Layer Graphical Models? Representation Inference Infrastructure Learning**We Need to Unify the Two**The real world is complex and uncertain Logic handles complexity Probability handles uncertainty**Artificial Intelligence**Planning Robotics Applications NLP Multi-Agent Systems Vision Interface Layer Markov Logic Representation Inference Infrastructure Learning

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