Proceedings of the seventh international conference (1990) on Machine learning
Conditional iterative proportional fitting for Gaussian distributions
Journal of Multivariate Analysis
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Editorial: Perceptual control theory and its application
International Journal of Human-Computer Studies
An Behavior-based Robotics
Recognizing Probabilistic Opponent Movement Models
RoboCup 2001: Robot Soccer World Cup V
Probabilistic State-Dependent Grammars for Plan Recognition
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Multiagent coordination by Extended Markov Tracking
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Prediction, Learning, and Games
Prediction, Learning, and Games
Dynamics based control with PSRs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Analysis and synthesis of switched linear control systems
Automatica (Journal of IFAC)
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In this paper we extend the control methodology based on Extended Markov Tracking (EMT) by providing the control algorithm with capabilities to calibrate and even partially reconstruct the environment's model. This enables us to resolve the problem of performance deterioration due to model incoherence, a problem faced in all model-based control methods. The new algorithm, Ensemble Actions EMT (EA-EMT), utilises the initial environment model as a library of state transition functions and applies a variation of prediction with experts to assemble and calibrate a revised model. By so doing, this is the first hybrid control algorithm that enables on-line adaptation within the egocentric control framework which dictates the control of an agent's perceptions, rather than an agent's environment state. In our experiments, we performed a range of tests with increasing model incoherence induced by three types of exogenous environment perturbations: catastrophic-the environment becomes completely inconsistent with the model, deviating-some aspect of the environment behaviour diverges compared to that specified in the model, and periodic-the environment alternates between several possible divergences. The results show that EA-EMT resolved model incoherence and significantly outperformed its EMT predecessor by up to 95%.