A comprehensive model and computational methods to improve Situation Awareness in Intelligence scenarios

Gaeta, Angelo; Loia, Vincenzo; Orciuoli, Francesco

doi:10.1007/s10489-021-02673-z

Cited by 6 publications

(1 citation statement)

References 62 publications

(97 reference statements)

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“…Using reinforcement learning techniques to adapt to evolving scenarios, D'Aniello et al [9] studied the cognitive map configuration issue by using an external desirability function to train and adapt the active goal orientation of the AGS algorithm. Loia et al [47], followed by Gaeta et al [48], proposed techniques to support Situation Awareness solutions using granular computing. Information granulation is a common activity that humans carry out with the intent of better understanding a problem, acting as an abstraction mechanism that reduces the conceptual load to comprehend the problem and offers better insight into situation awareness models.…”

Section: E Related Workmentioning

confidence: 99%

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: E Related Workmentioning

confidence: 99%