Automating Conflict Detection and Mitigation in Large-Scale IoT Systems

Author(s):  
Pavana Pradeep ◽  
Amitangshu Pal ◽  
Krishna Kant
Author(s):  
J Leonard ◽  
A Savvaris ◽  
A Tsourdos

The large-scale of unmanned aerial vehicle applications has escalated significantly within the last few years, and the current research is slowly hinting at a move from single vehicle applications to multivehicle systems. As the number of agents operating in the same environment grows, conflict detection and resolution becomes one of the most important factors of the autonomous system to ensure the vehicles’ safety throughout the completion of their missions. The work presented in this paper describes the implementation of the novel distributed reactive collision avoidance algorithm proposed in the literature, improved to fit a swarm of quadrotor helicopters. The original method has been extended to function in dense and crowded environments with relevant spatial obstacle constraints and deconfliction manoeuvres for high number of vehicles. Additionally, the collision avoidance is modified to work in conjunction with a dynamic close formation flight scheme. The solution presented to the conflict detection and Resolution problem is reactive and distributed, making it well suited for real-time applications. The final avoidance algorithm is tested on a series of crowded scenarios to test its performances in close quarters.


2015 ◽  
Vol 27 (8) ◽  
pp. 1617-1632 ◽  
Author(s):  
Ravi D. Mill ◽  
Ian Cavin ◽  
Akira R. O'Connor

Neural substrates of memory control are engaged when participants encounter unexpected mnemonic stimuli (e.g., a new word when told to expect an old word). The present fMRI study (n = 18) employed the likelihood cueing recognition task to elucidate the role of functional connectivity (fcMRI) networks in supporting memory control processes engaged by these unexpected events. Conventional task-evoked BOLD analyses recovered a memory control network similar to that previously reported, comprising medial prefrontal, lateral prefrontal, and inferior parietal regions. These were split by their differential affiliation to distinct fcMRI networks (“conflict detection” and “confirmatory retrieval” networks). Subsequent ROI analyses clarified the functional significance of this connectivity differentiation, with “conflict” network-affiliated regions specifically sensitive to cue strength, but not to response confidence, and “retrieval” network-affiliated regions showing the opposite pattern. BOLD time course analyses corroborated the segregation of memory control regions into “early” conflict detection and “late” retrieval analysis, with both processes underlying the allocation of memory control. Response specificity and time course findings were generalized beyond task-recruited ROIs to clusters within the large-scale fcMRI networks, suggesting that this connectivity architecture could underlie efficient processing of distinct processes within cognitive tasks. The findings raise important parallels between prevailing theories of memory and cognitive control.


2019 ◽  
Author(s):  
Jakub Šrol ◽  
Wim De Neys

One of the key components of the susceptibility to cognitive biases is the ability to monitor for conflict that may arise between intuitively cued “heuristic” answers and logical principles. While there is evidence that people differ in their ability to detect such conflicts, it is not clear which individual factors are driving these differences. In the present large-scale study (N = 399) we explored the role of cognitive ability, thinking dispositions, numeracy, cognitive reflection, and mindware instantiation (i.e. knowledge of logical principles) as potential predictors of individual differences in conflict detection ability and overall accuracy on a battery of reasoning problems. Results showed that mindware instantiation was the single best predictor of both conflict detection efficiency and reasoning accuracy. Cognitive reflection, thinking dispositions, numeracy, and cognitive ability played a significant but smaller role. The full regression model accounted for 40% of the variance in overall reasoning accuracy, but only 7% of the variance in conflict detection efficiency. We discuss the implications of these findings for popular process models of bias susceptibility.


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