Is this project an undergraduate, graduate, or faculty project?
Undergraduate
individual
What campus are you from?
Daytona Beach
Authors' Class Standing
Dmytro Kravchenko, Junior
Lead Presenter's Name
Dmytro Kravchenko
Faculty Mentor Name
Brian Watson
Abstract
Modern systems need the type of resilience we see in nature. By increasing resilience, we are increasing systems ability to adapt and successfully recover from difficult situations. By observing processes within a biological multi-agent system, we can develop approaches to increase resilience of a wide range of artificial systems, from transportation networks to homeland security. One source of design inspiration to improve artificial systems is natural systems. In this research, we are particularly interested in observing and documenting natural processes which allow multi- agent systems to detect and recover from faults, even when the fault is an unknown, faulted member (e.g. Byzantine Fault). We want to study such mechanisms in biological systems so that a multi- agent system, resilient to Byzantine fault can be designed. Many of these mechanisms have been already discovered in nature, however they have to be reviewed and consolidated from an engineering point of view in order to classify sources of possible inspiration. We are conducting an extensive literature review to gather these sources. Functional decomposition will be used to systematically break down and analyze the various aspects of biological inspirations for fault detection strategies. By adapting fault-agent detection mechanisms observed in nature, we will be able to maximize security and resilience of an artificial system. Today we cannot use biologically inspired design to solve this problem because we do not have a collection of sources of inspiration. The final result of this research will fill this gap with a database of collected sources of natural inspirations for fault detection strategies in nature.
Did this research project receive funding support from the Office of Undergraduate Research.
No
Identification and Classification of Fault Agent Detection Strategies in Nature
Modern systems need the type of resilience we see in nature. By increasing resilience, we are increasing systems ability to adapt and successfully recover from difficult situations. By observing processes within a biological multi-agent system, we can develop approaches to increase resilience of a wide range of artificial systems, from transportation networks to homeland security. One source of design inspiration to improve artificial systems is natural systems. In this research, we are particularly interested in observing and documenting natural processes which allow multi- agent systems to detect and recover from faults, even when the fault is an unknown, faulted member (e.g. Byzantine Fault). We want to study such mechanisms in biological systems so that a multi- agent system, resilient to Byzantine fault can be designed. Many of these mechanisms have been already discovered in nature, however they have to be reviewed and consolidated from an engineering point of view in order to classify sources of possible inspiration. We are conducting an extensive literature review to gather these sources. Functional decomposition will be used to systematically break down and analyze the various aspects of biological inspirations for fault detection strategies. By adapting fault-agent detection mechanisms observed in nature, we will be able to maximize security and resilience of an artificial system. Today we cannot use biologically inspired design to solve this problem because we do not have a collection of sources of inspiration. The final result of this research will fill this gap with a database of collected sources of natural inspirations for fault detection strategies in nature.