{"title":"基于贝叶斯网络的态势感知误差预测","authors":"Jean-Marc Salotti","doi":"10.1504/IJHFMS.2018.093174","DOIUrl":null,"url":null,"abstract":"A new method is proposed to predict situation awareness errors in training simulations. It is based on Endsley's model and the eight 'situation awareness demons' that she described. The predictions are determined thanks to a Bayesian network and noisy-or nodes. A maturity model is introduced to come up with the initialisation problem. The NASA behavioural competency model is also used to take individual differences into account.","PeriodicalId":417746,"journal":{"name":"International Journal of Human Factors Modelling and Simulation","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Bayesian network for the prediction of situation awareness errors\",\"authors\":\"Jean-Marc Salotti\",\"doi\":\"10.1504/IJHFMS.2018.093174\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new method is proposed to predict situation awareness errors in training simulations. It is based on Endsley's model and the eight 'situation awareness demons' that she described. The predictions are determined thanks to a Bayesian network and noisy-or nodes. A maturity model is introduced to come up with the initialisation problem. The NASA behavioural competency model is also used to take individual differences into account.\",\"PeriodicalId\":417746,\"journal\":{\"name\":\"International Journal of Human Factors Modelling and Simulation\",\"volume\":\"115 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Human Factors Modelling and Simulation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJHFMS.2018.093174\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Human Factors Modelling and Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJHFMS.2018.093174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian network for the prediction of situation awareness errors
A new method is proposed to predict situation awareness errors in training simulations. It is based on Endsley's model and the eight 'situation awareness demons' that she described. The predictions are determined thanks to a Bayesian network and noisy-or nodes. A maturity model is introduced to come up with the initialisation problem. The NASA behavioural competency model is also used to take individual differences into account.
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