TY - JOUR
T1 - Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees
AU - Xue, Jie
AU - Wu, Chaozhong
AU - Chen, Zhijun
AU - Van Gelder, P. H.A.J.M.
AU - Yan, Xinping
PY - 2019
Y1 - 2019
N2 - With the further development of marine and information technologies, ship intelligence, green policies and automation will become mainstream with global cargo ships. Ship labor costs increase every year, so for the foreseeable future, the number of experienced crew members will be greatly reduced as smart ship emergence accelerates. At present, there is no mature research system for the human-like piloting of smart ships. In this paper, we use an improved decision tree, which could address problems of fuzziness and uncertainty. This will allow us to study the decision mechanisms of different piloting behaviors in order to realize the automatic acquisition and representation of the pilot's decision-making knowledge in inbound ship analysis as well as the simulated reproduction of the pilot's behavior. The simulation results show that the piloting decision recognition model, based on the fuzzy Iterative Dichotomiser 3 (ID3) decision tree, possesses a high reasoning speed and can accurately identify current piloting behavior. This provides theoretical guidance and a feasibility basis for research into human-like piloting behavior and the realization of automatic smart ship piloting systems.
AB - With the further development of marine and information technologies, ship intelligence, green policies and automation will become mainstream with global cargo ships. Ship labor costs increase every year, so for the foreseeable future, the number of experienced crew members will be greatly reduced as smart ship emergence accelerates. At present, there is no mature research system for the human-like piloting of smart ships. In this paper, we use an improved decision tree, which could address problems of fuzziness and uncertainty. This will allow us to study the decision mechanisms of different piloting behaviors in order to realize the automatic acquisition and representation of the pilot's decision-making knowledge in inbound ship analysis as well as the simulated reproduction of the pilot's behavior. The simulation results show that the piloting decision recognition model, based on the fuzzy Iterative Dichotomiser 3 (ID3) decision tree, possesses a high reasoning speed and can accurately identify current piloting behavior. This provides theoretical guidance and a feasibility basis for research into human-like piloting behavior and the realization of automatic smart ship piloting systems.
KW - Classification rule
KW - Data mining
KW - Fuzzy decision trees
KW - Information entropy
KW - Piloting decision
KW - Smart ship
UR - http://www.scopus.com/inward/record.url?scp=85051392955&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2018.07.044
DO - 10.1016/j.eswa.2018.07.044
M3 - Article
AN - SCOPUS:85051392955
SN - 0957-4174
VL - 115
SP - 172
EP - 188
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -