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Research Area:

Artificial Intelligence

Description

Artificial intelligence is concerned with elucidating the principles behind intelligent behavior by creating artifacts (computer programs) that embody such principles. AI researchers at Brown tend to see probability and statistics as the primary mathematics in which these principles are to be expressed, while recognizing that various cognitive areas have quite different specifics. We also have a bias towards seeing the specific problems in which we are interested (e.g., vision, language, temporal reasoning, economic behavior, brain implants) as special instances of machine learning — that is, we believe that in a surprisingly wide variety of cases, the best way to get a program to solve some problem is to have it learn to solve the problem. Thus AI researchers at Brown share not just a common set of problems but a substantial set of tools with which to approach them.

Faculty

Michael J. Black
Eugene Charniak
Thomas Dean
Amy Greenwald
Chad Jenkins
Meinolf Sellmann
Erik Sudderth
Pascal Van Hentenryck

Topics or Projects

Robotics
Trading Agent Competition: Travel
Symmetry Breaking
Machine Learning
Graphical Models
Belief Propagation
Efficient Link Analysis
Human Motion Understanding
Planning Under Uncertainty
Computational Models of the Neocortex
Computer Vision
Facial Expression Recognition
Bayesian Inference
Online Stochastic Optimization
Natural Language Processing
No-Regret Learning and Games
Stochastic Models for Web Agents and the Web Environment
Trading Agent Competition: Supply Chain Management
Multiagent Learning in Games
Particle Filtering
Optimization - Hybrid Methods
Reinforcement Learning in Markov Games
Constraint Programming

Page Owner: Eugene Charniak Last Modified: Mon Oct 23 11:47:21 2006