Using Bayesian Networks to Model Competence of Lifeboat Coxswains

ABSTRACT: The assessment of lifeboat coxswain performance in operational scenarios representing offshore emergencies has been prohibitive due to risk. For this reason, human performance in plausible emergencies is difficult to predict due to the limited data that is available. The advent of lifeboat simulation provides a means to practice in weather conditions representative of an offshore emergency. In this paper, we present a methodology to create probabilistic models to study this new problem space using Bayesian Networks (BNs) to formulate a model of competence. We combine expert input and simulator data to create a BN model of the competence of slow-speed maneuvering (SSM). We demonstrate how the model is improved using data collected in an experiment designed to measure performance of coxswains in an emergency scenario. We illustrate how this model can be used to predict performance and diagnose background information about the student. The methodology demonstrates the use of simulation and probabilistic methods to increase domain awareness where limited data is available. We discuss how the methodology can be applied to improve predictions and adapt training using machine learning.

KEYWORDS: Emergency Scenario, Bayesian Networks, Lifeboat, Machine Learning Method, Lifeboat Coxswains, Model Competence, Bayesian Networks to Model Competence, Slow-Speed Maneuvering (SSM)

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Citation note:

Billard R., Smith J., Masharraf M., Veitch B.: Using Bayesian Networks to Model Competence of Lifeboat Coxswains. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 14, No. 3, doi:10.12716/1001.14.03.09, pp. 585-594, 2020

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