Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees

Journal article (2019)

Authors

J. Xue Safety and Security Science - TPM , Wuhan University of Technology, National Engineering Research Center for Water Transport Safety (WTSC)
Chaozhong Wu National Engineering Research Center for Water Transport Safety (WTSC), Wuhan University of Technology
Zhijun Chen Wuhan University of Technology, National Engineering Research Center for Water Transport Safety (WTSC)
P.H.A.J.M. van Gelder Safety and Security Science - TPM
Xinping Yan Wuhan University of Technology
Research Group
Safety and Security Science (TPM) (TU Delft)
Data mining Classification rule Fuzzy decision trees Information entropy Piloting decision Smart ship
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Published Date

2019

Language

English

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

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.