Luke, Sean


Unlearning from Demonstration

AAAI Conferences

When doing learning from demonstration, it is often the case that the demonstrator provides corrective examples to fix errant behavior by the agent or robot. We present a set of algorithms which use this corrective data to identify and remove noisy examples in datasets which caused errant classifications, and ultimately errant behavior. The objective is to actually modify the source datasets rather than solely rely on the noise-insensitivity of the classification algorithm. This is particularly useful in the sparse datasets often found in learning from demonstration experiments. Our approach tries to distinguish between noisy misclassification and mere undersampling of the learning space. If errors are a result of misclassification, we potentially remove the responsible points and update the classifier. We demonstrate our method on UCI Machine Learning datasets at different levels of sparsity and noise, using decision trees, K-Nearest-Neighbor, and support vector machines.


Reports on the 2004 AAAI Fall Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence presented its 2004 Fall Symposium Series Friday through Sunday, October 22-24 at the Hyatt Regency Crystal City in Arlington, Virginia, adjacent to Washington, DC. The symposium series was preceded by a one-day AI funding seminar. The topics of the eight symposia in the 2004 Fall Symposia Series were: (1) Achieving Human-Level Intelligence through Integrated Systems and Research; (2) Artificial Multiagent Learning; (3) Compositional Connectionism in Cognitive Science; (4) Dialogue Systems for Health Communications; (5) The Intersection of Cognitive Science and Robotics: From Interfaces to Intelligence; (6) Making Pen-Based Interaction Intelligent and Natural; (7) Real- Life Reinforcement Learning; and (8) Style and Meaning in Language, Art, Music, and Design.


Reports on the 2004 AAAI Fall Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence presented its 2004 Fall Symposium Series Friday through Sunday, October 22-24 at the Hyatt Regency Crystal City in Arlington, Virginia, adjacent to Washington, DC. The symposium series was preceded by a one-day AI funding seminar. The topics of the eight symposia in the 2004 Fall Symposia Series were: (1) Achieving Human-Level Intelligence through Integrated Systems and Research; (2) Artificial Multiagent Learning; (3) Compositional Connectionism in Cognitive Science; (4) Dialogue Systems for Health Communications; (5) The Intersection of Cognitive Science and Robotics: From Interfaces to Intelligence; (6) Making Pen-Based Interaction Intelligent and Natural; (7) Real- Life Reinforcement Learning; and (8) Style and Meaning in Language, Art, Music, and Design.


Three RoboCup Simulation League Commentator Systems

AI Magazine

Three systems that generate real-time natural language commentary on the RoboCup simulation league are presented, and their similarities, differences, and directions for the future discussed. Although they emphasize different aspects of the commentary problem, all three systems take simulator data as input and generate appropriate, expressive, spoken commentary in real time.


Three RoboCup Simulation League Commentator Systems

AI Magazine

Three systems that generate real-time natural language commentary on the RoboCup simulation league are presented, and their similarities, differences, and directions for the future discussed. Although they emphasize different aspects of the commentary problem, all three systems take simulator data as input and generate appropriate, expressive, spoken commentary in real time.