Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees. (January 2019)

• Record Type: Journal Article
• Authors: Xue, Jie; Wu, Chaozhong; Chen, Zhijun; Van Gelder, P.H.A.J.M.; Yan, Xinping
• Is Part Of: Expert systems with applications. Volume 115(2019)
• Page Start: 172
• Page End: 188
• Publication Date: 2019-01
• Subjects: Fuzzy decision trees -- Piloting decision -- Smart ship -- Classification rule -- Data mining -- Information entropy; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.07.044
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel piloting decision recognition model for fuzziness and uncertainty problems. Automatic acquisition and representation of the pilot's decision-making knowledge. A flexible method that can mine the key factors which affect piloting decisions. The standardization principle of piloting decision-making factors is proposed. A feasibility basis for the realization of automatic smart ship piloting systems. 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.