Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
A tennis serve and upswing learning robot based on bi-directional theory
Neural Networks - Special issue on neural control and robotics: biology and technology
An Introduction to Variational Methods for Graphical Models
Machine Learning
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper addresses agents' intentions as building blocks of imitation learning that abstract local situations of the agent, and proposes a hierarchical hidden Markov model (HMM) to represent cooperative behaviors of teamworks. The key of the proposed model is introduction of gate probabilities that restrict transition among agents' intentions according to others' intentions. Using these probabilities, the framework can control transitions flexibly among basic behaviors in a cooperative behavior.