Autonomous Robot Failure Recognition Design using Multi-Objective Genetic Programming

@inproceedings{Zhang2006, abstract = {An evolutionary autonomous failure recognition approach is presented using multi-objective genetic programming in this paper. It is compared with the conventional robot failure classification algorithm. Detailed analysis of the evolved feature extractors is tempted on investigated problems. We conclude MOGP is an effective and practical way to automate the process of failure recognition system design with better recognition accuracy and more flexibility via optimizing feature extraction stage.}, author = {Zhang, Yang }, booktitle = {Machine Learning and Cybernetics, 2006 International Conference on}, citeulike-article-id = {2762309}, doi = {http://dx.doi.org/10.1109/ICMLC.2006.258378}, journal = {Machine Learning and Cybernetics, 2006 International Conference on}, pages = {4563--4568}, posted-at = {2008-05-06 20:08:18}, priority = {2}, title = {Autonomous Robot Failure Recognition Design using Multi-Objective Genetic Programming}, url = {http://dx.doi.org/10.1109/ICMLC.2006.258378}, year = {2006} }

TY - CONF ID - Zhang2006 L3 - citeulike-article-id:2762309 N2 - An evolutionary autonomous failure recognition approach is presented using multi-objective genetic programming in this paper. It is compared with the conventional robot failure classification algorithm. Detailed analysis of the evolved feature extractors is tempted on investigated problems. We conclude MOGP is an effective and practical way to automate the process of failure recognition system design with better recognition accuracy and more flexibility via optimizing feature extraction stage. JF - Machine Learning and Cybernetics, 2006 International Conference on EP - 4568 TI - Autonomous Robot Failure Recognition Design using Multi-Objective Genetic Programming T2 - Machine Learning and Cybernetics, 2006 International Conference on SP - 4563 AU - Zhang, Yang PY - 2006/// UR - http://dx.doi.org/10.1109/ICMLC.2006.258378 ER -