Is this project an undergraduate, graduate, or faculty project?
Undergraduate
individual
What campus are you from?
Daytona Beach
Authors' Class Standing
Sophomore
Lead Presenter's Name
Grace Gratton
Faculty Mentor Name
Dr. Bryan Watson
Abstract
Multi-agent systems have several advantages over centralized systems of control and are becoming more heavily relied upon by systems. Consensus algorithms are a fundamental element of multi-agent systems, but current methods of consensus have a low robustness to faulted agents, endangering the integrity of the system. Biologically Inspired Design previously inspired a state-of-the-art consensus algorithm with resilience of up to 20% faulted agents within a population, called the Synchronous Hatching Consensus Algorithm. This article further tests the algorithm by implementing faulted agents reporting false negatives, rather than the original false positives, to see if the model remains robust. To test the proposed algorithm an Agent-Based, ANYLOGIC model was tested against 0, 1, 5, 10, 15, and 20 ‘false negative’ faulted agents across four varying environments. The robustness of the algorithm was measured by the time to consensus for 66% and the percentage of runs that failed to reach consensus. Three separate tests were conducted to determine if the model is robust to faulted agents reporting false positives. It was found that two of three tests deemed the model not robust. An uncertainty and sensitivity analysis was then performed to determine the uncertainty of failure to reach 66% consensus and the cause of uncertainty in the results. The total probability to not reach consensus was 59%, and the highest cause of this failure was the rate of change of the environment. The number of faulted agents had the second highest impact upon the uncertainty. Thus, we recommend this algorithm be implemented in an environment that quickly reaches its decision threshold and interacts with predominantly false positive faulted agents.
Did this research project receive funding support from the Office of Undergraduate Research.
Yes, SURF
Evaluating a Biologically-Inspired Multi-Agent System Consensus Algorithm for Robustness to False Negatives
Multi-agent systems have several advantages over centralized systems of control and are becoming more heavily relied upon by systems. Consensus algorithms are a fundamental element of multi-agent systems, but current methods of consensus have a low robustness to faulted agents, endangering the integrity of the system. Biologically Inspired Design previously inspired a state-of-the-art consensus algorithm with resilience of up to 20% faulted agents within a population, called the Synchronous Hatching Consensus Algorithm. This article further tests the algorithm by implementing faulted agents reporting false negatives, rather than the original false positives, to see if the model remains robust. To test the proposed algorithm an Agent-Based, ANYLOGIC model was tested against 0, 1, 5, 10, 15, and 20 ‘false negative’ faulted agents across four varying environments. The robustness of the algorithm was measured by the time to consensus for 66% and the percentage of runs that failed to reach consensus. Three separate tests were conducted to determine if the model is robust to faulted agents reporting false positives. It was found that two of three tests deemed the model not robust. An uncertainty and sensitivity analysis was then performed to determine the uncertainty of failure to reach 66% consensus and the cause of uncertainty in the results. The total probability to not reach consensus was 59%, and the highest cause of this failure was the rate of change of the environment. The number of faulted agents had the second highest impact upon the uncertainty. Thus, we recommend this algorithm be implemented in an environment that quickly reaches its decision threshold and interacts with predominantly false positive faulted agents.