10-803 markov logic networks instructor: pedro domingos

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!

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.

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)

Class begins here.

AI: The First 100 Years

IQ HumanIntelligence

ArtificialIntelligence

1956 20562006

AI: The First 100 Years

IQ HumanIntelligence

ArtificialIntelligence

1956 20562006

AI: The First 100 Years

IQ HumanIntelligence

ArtificialIntelligence

1956 20562006

The Interface Layer

Interface Layer

Applications

Infrastructure

Networking

Interface Layer

Applications

Infrastructure

Internet

RoutersProtocols

WWWEmail

Databases

Interface Layer

Applications

Infrastructure

Relational Model

QueryOptimization

TransactionManagement

ERP

OLTP

CRM

Programming Systems

Interface Layer

Applications

Infrastructure

High-Level Languages

CompilersCodeOptimizers

Programming

Hardware

Interface Layer

Applications

Infrastructure

VLSI Design

VLSI modules

Computer-Aided Chip Design

Architecture

Interface Layer

Applications

Infrastructure

Microprocessors

BusesALUs

OperatingSystems

Compilers

Operating Systems

Interface Layer

Applications

Infrastructure

Virtual machines

Hardware

Software

Human-Computer Interaction

Interface Layer

Applications

Infrastructure

Graphical User Interfaces

Widget Toolkits

Productivity Suites

Artificial Intelligence

Interface Layer

Applications

Infrastructure

Representation

Learning

Inference

NLP

Planning

Multi-AgentSystemsVision

Robotics

Artificial Intelligence

Interface Layer

Applications

Infrastructure

Representation

Learning

Inference

NLP

Planning

Multi-AgentSystemsVision

Robotics

First-Order Logic?

Artificial Intelligence

Interface Layer

Applications

Infrastructure

Representation

Learning

Inference

NLP

Planning

Multi-AgentSystemsVision

Robotics

Graphical Models?

Logical and Statistical AI

Field Logical approach

Statistical approach

Knowledge representation

First-order logic Graphical models

Automated reasoning

Satisfiability testing

Markov chain Monte Carlo

Machine learning Inductive logic programming

Neural networks

Planning Classical planning

Markov decision processes

Natural language

processing

Definite clause grammars

Prob. context-free grammars

We Need to Unify the Two

The real world is complex and uncertain Logic handles complexity Probability handles uncertainty

Artificial Intelligence

Interface Layer

Applications

Infrastructure

Representation

Learning

Inference

NLP

Planning

Multi-AgentSystemsVision

Robotics

Markov Logic