Coordinated machine learning and decision support for situation awareness

Brannon, Nathan Gregory; Seiffertt, John; Draelos, Timothy J.; Wunsch, Donald C.

doi:10.1016/j.neunet.2009.03.013

Cited by 34 publications

(30 citation statements)

References 20 publications

(19 reference statements)

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“…Many studies have reported that machine learning techniques could be an effective method for intelligent prediction by extracting rules from previous data to generate new assessment results (Lu et al, 2008a), but their use has been limited, possibly because of the lack of rich training data for SA (Brannon et al, 2009). Kim and Seong (2006a) developed a quantitative model based on Bayesian inference and information theory, and described the process of knowledge-driven monitoring and the revision of operators' understanding of their environments.…”

Section: Situation Assessmentmentioning

confidence: 99%

“…Efforts have been made to develop new SA approaches for military purposes and maritime security (Baader et al, 2009;Farahbod et al, 2011), and other studies have used machine learning, expert systems and ontology (Brannon et al, 2009;Naderpour and Lu, 2012a, b;Nguyen et al, 2009). However, none of these is appropriate for supporting operators" SA in abnormal situations.…”

Section: Introductionmentioning

confidence: 99%

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A situation risk awareness approach for process systems safety

Naderpour

Zhang

2014

Safety Science

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Abstract:Promoting situation awareness is an important design objective for a wide variety of domains, especially for process systems where the information flow is quite high and poor decisions may lead to serious consequences. In today"s process systems, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in safe conditions. This paper proposes a situation risk awareness approach for process systems safety where the effect of ever-increasing situational complexity on human decision-makers is a concern. To develop the approach, two important aspects -addressing hazards that arise from hardware failure and reducing human error through decision-making -have been considered. The proposed situation risk awareness approach includes two major elements: an evidence preparation component and a situation assessment component. The evidence preparation component provides the soft evidence, using a fuzzy partitioning method, that is used in the subsequent situation assessment component. The situation assessment component includes a situational network based on dynamic Bayesian networks to model the abnormal situations, and a fuzzy risk estimation method to generate the assessment result. A case from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of the proposed approach.

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Section: Situation Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A situation risk awareness approach for process systems safety

Naderpour

Zhang

2014

Safety Science

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“…This implies collecting the right multi-domain information across a net-centric environment for shared awareness and presenting results for the human to understand and make quick decisions. Any new approach must efficiently bring together the human operator, sensor equipment data, and real world events to provide a subset of actionable information [9].…”

Section: Expert Systemsmentioning

confidence: 99%

A fuzzy dual expert system for managing situation awareness in a safety supervisory system

Naderpour

2012

2012 IEEE International Conference on Fuzzy Systems

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Safety supervisory systems continue to increase in degree of automation and complexity as operators are decreasing. As a result, each operator must be able to comprehend and respond to an ever increasing amount of available risky status and alert information. They generally have no difficulty in performing their tasks physically but they are stressed by the task of understanding what is going on in the situation. So in the last two decades, situation awareness has been recognized as a critical foundation for successful decision making across a broad range of complex and dynamic systems. This paper develops a fuzzy dual expert system based approach to enhance situation awareness. The proposed approach has ability to support the operators' understanding and assessing the situations, and to deal with uncertainties, applying fuzzy risk assessment concepts.

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“…GAs have been used, for instance, for mission planning ( [9]) and situational awareness ( [10]). Other machine learning methods have been used for situational awareness, such as particle filters that have been used for state estimation for Mars Rovers ( [11]) and Adaptive Resonance Theory (ART) that has been used for information fusion ( [12]). We use GAs for parameter optimization, specifically to find the weights of the links that connect simple beliefs, complex beliefs, and future beliefs.…”

Section: Related Workmentioning

confidence: 99%

Learning Belief Connections in a Model for Situation Awareness

Gini

Hoogendoorn

Lambalgen

2011

Lecture Notes in Computer Science

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Abstract.Situational awareness is critical in many human tasks, especially in cases where humans have to make decisions fast and where the result of their decisions might affect their life. This paper addresses the problem of learning optimal values for the parameters of a situational awareness model. The model is a complex network with nodes connected by links with weights, which connect observations to simple beliefs, such as "there is a contact", to complex belief, such as "the contact is hostile", and to future beliefs, such as "it is possible the pilot is being targeted". The model has been built and validated by human experts in the domain of F16 fighter pilots and is used to study human decision making. Given the complexity of the model, there is a need to learn appropriate weights for the connections, which, in turn, affect the activation levels of the beliefs. We propose the use of a genetic algorithm and of a sensitivity based approach to learn the weights in the model. Extensive experimental results are included.

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Coordinated machine learning and decision support for situation awareness

Cited by 34 publications

References 20 publications

A situation risk awareness approach for process systems safety

A situation risk awareness approach for process systems safety

A fuzzy dual expert system for managing situation awareness in a safety supervisory system

Learning Belief Connections in a Model for Situation Awareness

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