Welcome to the UW CSE AI Web site! We are active in all major areas of AI, including machine learning, natural language processing, planning, reasoning, robotics, and vision. We are ranked in the Top Five departments in AI by U.S. News & World Report. We work closely with the Database Systems, Information Retrieval, and Intelligent Internet Systems group and the Computer Graphics, Computer Vision and Animation group, as well as with collaborators throughout UW and at nearby companies like Microsoft, Amazon, Boeing, and Google.

AI at UW is a growing community with a large number of current projects as well as past successes. These pages give you a sampler of ongoing and past work. Enjoy!

Ubiquitous AI Robotics Planning Neural Systems Machine Vision Machine Learning and Data Mining Learning Human Behavior Knowledge Representation and Reasoning Intelligent User Interfaces Intelligent Internet Systems

AI Demos

Cross-lingual Image Search
TextRunner

New projects!

MDP Solvers That Scale
Intelligence in Wikipedia Project
Sensing and Modeling Dynamic Social Networks
ARNAULD: Preference Elicitation For Interface Optimization
Markov Logic Networks: Probabilistic first-order knowledge bases
Planning for Concurrent Durative Uncertain Actions: Augmenting Markov Decision Processes to handle concurrent temporally-extended actions.
Spectral graph partitioning: Clustering and learning in networks of symmetric and asymmetric relationships.
KnowItAll: Automated, domain-independent, web-scale information extraction and evaluation.
Statistical Relational Learning: Learning from noisy data in rich representations.
Collective Knowledge Bases: Merging knowledge from a multitude of sources.
Large-Scale Machine Learning: Mining massive data streams.
Assisted Cognition: Computer systems to aid people with Alzheimer's disease.
CORE: Optimizing search algorithms using Bayesian models to predict running time.

In the list below current projects are marked with a while past work is noted with a .

Intelligent Internet Systems

Intelligence in Wikipedia Project
KnowItAll: Automated, domain-independent, web-scale information extraction and evaluation.
Opine
Tukwila: Data integration system for heterogeneous data on the web.
Mulder: A natural-language question-answering service that Believes.
Tiramisu: Declarative Web-site management.
The Internet Softbot: The mother of all intelligent internet systems.
Site popularity meta-search: Re-ranking results of web engines using web page popularity.
Grouper: Document clustering for improved search results on the web.
ReferralWeb: Explore the social networks that exist on the Web.

Intelligent and Personalizable User Interfaces

ARNAULD: Preference Elicitation For Interface Optimization
SUPPLE: Automatic Generation of User Interfaces
Provably Reliable Question-Answering Interfaces: Natural language interfaces that are guaranteed to answer "easy questions" correctly.
Adaptive Web sites: Sites that improve their organization by learning from visitor usage.
Web Site Personalizers: Intermediaries between servers and visitors that automatically adapt and customize content for wireless web visitors.
Programming by Demonstration: Using AI techniques to improve user interfaces.
Adaptive interfaces for machine learning systems.

Knowledge Representation and Reasoning

CORE: Optimizing search algorithms using Bayesian models to predict running time.
Structural Modeling for Anatomy: Representing knowledge about human anatomy.
Walksat: Stochastic local search for satisfiability.

Learning Human Behavior

Sensing and Modeling Dynamic Social Networks
Activity Recognition
Assisted Cognition: Computer systems to aid people with Alzheimer's Disease.

Machine Learning and Data Mining

Machine Learning in Biology
Markov Logic Networks: Probabilistic first-order knowledge bases
Statistical Relational Learning: Learning from noisy data in rich representations
Collective Knowledge Bases: Merging knowledge from a multitude of sources
Large-Scale Machine Learning: Mining massive data streams
Belief networks.
Spectral graph partitioning: Clustering and learning in networks of symmetric and asymmetric relationships
Statistical machine learning.
LSD: Learning source descriptions for data integration.
CMM: Converting model ensembles into a single comprehensible model.
RISE: High-performance concept learner, unifies rule induction and instance-based learning.
Naive Bayes.
MetaCost: Making error-based learners cost-sensitive.
Process-Oriented Evaluation: Avoiding overfitting by estimating a hypothesis' generalization error as a function of the search process that led to it.

Machine Vision

3D object recognition.
Content-based image retrieval.

Neural Systems

Probabilistic models of the brain.
Spike-based computing and learning: For instance, Temporal sequence learning.
Learning algorithms for vision: For instance, Invariant coding under image transformations.
Brain-computer interfaces: EEG-based systems that allow completely paralyzed patients to interact with a computer.

Planning

Robotics

Monte Carlo Localization (MCL): Particle filters for state estimation in mobile robotics.
Multirobot systems: Navigation and coordination of multiple robots.
Mobile robot control: Probabilistic techniques that can handle position and sensor uncertainty.
Museum tour-guides: Rhino and Minerva guide visitors through crowded museums.

Ubiquitous AI

Assisted Cognition: Computer systems to aid people with Alzheimer's Disease.