Case learning for CBR-based collision avoidance systems

Liu, Yuhong; Yang, Chunsheng; Yang, Yongchun; Lin, Fuhua; Du, Xiaoxu; Ito, Takayuki

doi:10.1007/s10489-010-0262-z

Cited by 19 publications

(6 citation statements)

References 22 publications

(20 reference statements)

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“…However, it is interesting to note that in this digitalization domain, most of the works are related to the shipping industry; Ten of the 19 works pertain to AI in the maritime industry, whereas on robotics, there was only one. Of these ten, five are studying matters related to navigational aids [ 53 , 54 , 55 , 56 , 57 ]; three are works on risk management [ 58 , 59 , 60 ]; one is an AI application for maritime economics [ 61 ]; and the last is on using AI to improve security in shipping [ 62 ].…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…These variables were then quantified by using fuzzy set theory. Finally, the last study on fleet risk management, performed by Liu et al [ 60 ], used case-based reasoning for the design of an SW that provided assistance with collision avoidance by using historical data from the records of maritime affairs.…”

Section: Digitalization On Maritime Transport: the Situation At Thmentioning

confidence: 99%

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Toward Digitalization of Maritime Transport?

Sanchez-Gonzalez

Díaz-Gutiérrez

Leo

et al. 2019

Sensors

124

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Although maritime transport is the backbone of world commerce, its digitalization lags significantly behind when we consider some basic facts. This work verifies the state-of-the-art as it currently applies to eight digital domains: Autonomous vehicles and robotics; artificial intelligence; big data; virtual reality, augmented and mixed reality; internet of things; the cloud and edge computing; digital security; and 3D printing and additive engineering. It also provides insight into each of the three sectors into which this industry has been divided: Ship design and shipbuilding; shipping; and ports. The work, based on a systematic literature review, demonstrates that there are domains on which almost no formal study has been done thus far and concludes that there are major areas that require attention in terms of research. It also illustrates the increasing interest on the subject, arising from the necessity of raising the maritime transport industry to the same level of digitalization as other industries.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Digitalization On Maritime Transport: the Situation At Thmentioning

confidence: 99%

Toward Digitalization of Maritime Transport?

Sanchez-Gonzalez

Díaz-Gutiérrez

Leo

et al. 2019

Sensors

124

View full text Add to dashboard Cite

show abstract

“…In [5], authors compare a probabilistic CBR approach with other well-known classification algorithms. This technique has been applied to other real-world domains such as collision avoidance systems [20].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Using a case-based reasoning approach for trading in sports betting markets

Alberola

García-Fornes

2012

Appl Intell

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The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demnostrate that they can outperform even the bevahior of high-experienced humans. To achieve this goal, we propose a case-based reasoning model for trading in sports betting markets, which is integrated in an agent to provide it with the capabilities to take trading decisions based on future odd predictions. In order to test the performance of the system, we compare trading decisions taken by the agent with trading decisions taken by human traders when they compete in real sports betting markets.

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“…The risk is an important indicator for navigator to manuvuour the ship. It is computed based on navigation theory (Liu et al, 2012). We will explain this indicator in the later section.…”

Section: Overview Of Intelligent Navigation Tutoring Systemsmentioning

confidence: 99%

Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems

Yang

Chiang

Cheng

et al. 2021

Journal of Educational Computing Research

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Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student models for specific educational systems. However, systematically developing the data-driven student models from the educational data collected over prior educational experiences remains a challenge. We proposed a systematic and comprehensive machine learning-based modeling methodology to develop high-performance predictive student models from the historical educational data to address this issue. This methodology addresses the fundamental modeling issues, from data processing, to modeling, to model deployment. The said methodology can help developing student models for intelligent educational systems. After a detailed description of the proposed machine learning-based methodology, we introduce its application to an intelligent navigation tutoring system. Using the historical data collected in intelligent navigation tutoring systems, we conduct large-scale experiments to build the student models for training systems. The preliminary results proved that the proposed methodology is useful and feasible in developing the high-performance models for various intelligent education systems.

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Case learning for CBR-based collision avoidance systems

Cited by 19 publications

References 22 publications

Toward Digitalization of Maritime Transport?

Toward Digitalization of Maritime Transport?

Using a case-based reasoning approach for trading in sports betting markets

Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems

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