Learning Belief Connections in a Model for Situation Awareness

Gini, Maria; Hoogendoorn, Mark; Lambalgen, Rianne van

doi:10.1007/978-3-642-25044-6_30

Cited by 4 publications

(2 citation statements)

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“…The literature shows that the automation of the SABDM model using machine learning techniques presents good results in model parameter configuration [8], [13] and in the model's goal selection [9]. This study extends this knowledge by proposing automating the initial model parameter adjustments and evolutionary maintenance under environmentally changing conditions using reinforcement learning techniques.…”

Section: Introductionmentioning

confidence: 79%

“…The literature presents automation and enhancements to situation-awareness decision-making applications using distinct techniques. Gini et al [13] employed genetic algorithms to automatically learn the situation awareness model's mental or cognitive map (belief network) parameters. Koopmanschap et al [8] evaluated hill climbing and evolutionary genetic algorithms.…”

Section: E Related Workmentioning

confidence: 99%

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A Comparative Study of Situation Awareness-Based Decision-Making Model Reinforcement Learning Adaptive Automation in Evolving Conditions

2023

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Situation-awareness-based decision-making (SABDM) models constructed using cognitive maps and goal-direct task analysis techniques have been successfully used in decision support systems in safety-critical and mission-critical environments such as air traffic control and electrical energy distribution. Reinforcement learning (RL) and other machine learning techniques are used to automate situational awareness mental model parameter adjustments, reducing the expert work on the initial configuration and long-term maintenance without affecting the mental model's structure and maintaining the situationawareness-based decision-making model's cognitive and explainability characteristics. Real-world models should evolve to cope with changes in the environmental conditions. This study evaluates the application of reinforcement learning as an online adaptive technique to adjust the situational-awareness mental model's parameters under evolving conditions. We conducted evaluation experiments using real-world public datasets to compare the performance of the SABDM model with that of the reinforcement learning adaptation technique (SABDM/RL) and other adaptive machine learning methods under distinct concept drift evolving conditions. We measured the techniques' overall and dynamic performance to understand how well they adapt to evolving environmental conditions. The experiments show that SABDM/RL performs similarly to modern online adaptive machine learning classification methods with the support of concept drift detection techniques while maintaining the mental model strength of the situation awarenessbased systems.

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Section: Introductionmentioning

confidence: 79%

Section: E Related Workmentioning

confidence: 99%