TY - JOUR
T1 - Linguistic Decision Making For Robot Route Learning
AU - He, Hongmei
AU - McGinnity, TM
AU - Coleman, SA
AU - Gardiner, B
PY - 2014/1
Y1 - 2014/1
N2 - Machine learning enables the creation of a nonlinearmapping that describes robot-environment interaction, whilecomputing linguistics make the interaction transparent. In thispaper, we develop a novel application of a linguistic decision treefor a robot route learning problem by dynamically deciding therobot’s behaviour, which is decomposed into atomic actions in thecontext of a specified task.We examine the real-time performanceof training and control of a Linguistic Decision Tree, and explorethe possibility of training a machine learning model in anadaptive system without dual CPUs for parallelisation of trainingand control. A quantified evaluation approach is proposed, anda score is defined for the evaluation of a model’s robustnessregarding the quality of training data. Compared with thenon-linear system identification NARMAX model structure withoffline parameter estimation, the linguistic decision tree modelwith online LID3 learning achieves much better performance,robustness and reliability.
AB - Machine learning enables the creation of a nonlinearmapping that describes robot-environment interaction, whilecomputing linguistics make the interaction transparent. In thispaper, we develop a novel application of a linguistic decision treefor a robot route learning problem by dynamically deciding therobot’s behaviour, which is decomposed into atomic actions in thecontext of a specified task.We examine the real-time performanceof training and control of a Linguistic Decision Tree, and explorethe possibility of training a machine learning model in anadaptive system without dual CPUs for parallelisation of trainingand control. A quantified evaluation approach is proposed, anda score is defined for the evaluation of a model’s robustnessregarding the quality of training data. Compared with thenon-linear system identification NARMAX model structure withoffline parameter estimation, the linguistic decision tree modelwith online LID3 learning achieves much better performance,robustness and reliability.
KW - Linguistic decision tree
KW - Task decomposition
KW - Atomic action
KW - Dynamic behaviour decision
KW - Robot route learning.
UR - https://pure.ulster.ac.uk/en/publications/linguistic-decision-making-for-robot-route-learning-3
U2 - 10.1109/TNNLS.2013.2258037
DO - 10.1109/TNNLS.2013.2258037
M3 - Article
VL - 25
SP - 203
EP - 215
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 1
ER -