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H. He, Martin McGinnity, Sonya Coleman, and Bryan Gardiner (2013)

Linguistic Decision Making For Robot Route Learning

In: IEEE Transactions on Neural Networks and Learning Systems.

Machine learning enables the creation of a nonlinear mapping that describes robot-environment interaction, while computing linguistics make the interaction transparent. In this paper, we develop a novel application of a linguistic decision tree for a robot route learning problem by dynamically deciding the robot’s behaviour, which is decomposed into atomic actions in the context of a specified task. We examine the real-time performance of training and control of a Linguistic Decision Tree, and explore the possibility of training a machine learning model in an adaptive system without dual CPUs for parallelisation of training and control. A quantified evaluation approach is proposed, and a score is defined for the evaluation of a model’s robustness regarding the quality of training data. Compared with the non-linear system identification NARMAX model structure with offline parameter estimation, the linguistic decision tree model with online LID3 learning achieves much better performance, robustness and reliability.