C4.5: programs for machine learning
C4.5: programs for machine learning
Synthetic robot language development
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning in the presence of concept drift and hidden contexts
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Machine Learning - Special issue on context sensitivity and concept drift
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Statistical Learning for Humanoid Robots
Autonomous Robots
Building a Multimodal Human-Robot Interface
IEEE Intelligent Systems
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
A Probabilistic Model for Understanding and Comparing Collective Aggregation Mechansims
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Imitation in animals and artifacts
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Learning Movement Sequences from Demonstration
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Decision-tree based error correction for statistical phrase break prediction in Korean
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Motion capture from demonstrator's viewpoint and its application to robot teaching
Journal of Robotic Systems
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
Hidden state and reinforcement learning with instance-based stateidentification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We have proposed a new repetition framework for vision-based behavior imitation by a sequence of multiple humanoid robots, introducing an on-line method for delimiting a time-varying context. This novel approach investigates the ability of a robot "student" to observe and imitate a behavior from a "teacher" robot; the student later changes roles to become the "teacher" for a naïve robot. For the many robots that already use video acquisition systems for their real-world tasks, this method eliminates the need for additional communication capabilities and complicated interfaces. This can reduce human intervention requirements and thus enhance the robots' practical usefulness outside the laboratory. Articulated motions are modeled in a three-layer method and registered as learned behaviors using color-based landmarks. Behaviors were identified on-line after each iteration by inducing a decision tree from the visually acquired data. Error accumulated over time, creating a context drift for behavior identification. In addition, identification and transmission of behaviors can occur between robots with differing, dynamically changing configurations. ITI, an on-line decision tree inducer in the C4.5 family, performed well for data that were similar in time and configuration to the training data but the greedily chosen attributes were not optimized for resistance to accumulating error or configuration changes. Our novel algorithm, OLDEX identified context changes on-line, as well as the amount of drift that could be tolerated before compensation was required. OLDEX can thus identify time and configuration contexts for the behavior data. This improved on previous methods, which either separated contexts off-line, or could not separate the slowly time-varying context into distinct regions at all. The results demonstrated the feasibility, usefulness, and potential of our unique idea for behavioral repetition and a propagating learning scheme.