Research Abstracts - Foundations of Artificial Intelligence and Learning

MIT Artificial Intelligence Laboratory
Research Abstracts — 2000

Applied Artificial Intelligence and Learning Biologically Inspired Robots and Models Computational Support for People Foundations of Artificial Intelligence and Learning Medical Robots	Medical Vision Mobile Robotics New Models of Computation Vision and Sound Applied to People and Activity Vision Techniques

Foundations of Artificial Intelligence and Learning

Joanna Bryson & Lynn Andrea Stein	"Behavior Oriented Design: Methodology for Developing Adaptive Intelligent Agents"
Terran Lane & Leslie Pack Kaelbling	"Learning and Planning in Huge Uncertain Environments"
Tim Oates & Leslie Pack Kaelbling	"Learning Rich, Tractable Models of the Real World"
Ryan Rifkin & Michelle Nadermann	"SvmFu: Software For SVMs"
Christian Shelton	"Balancing Multiple Sources of Reward in Reinforcement Learning"
William Smart & Leslie Pack Kaelbling	"Making Reinforcement Learning Work on Real Robots"
Martin Szummer	"Learning from Partially Labeled Data"
Kimberly G. Waters	"Combining Strategies for Planning in Huge Uncertain Domains"
Kimberly G. Waters