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Understanding Emergent Behavior in Complex Systems & System-of-Systems: How to Leverage Machine Learning Models


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A complex system is characterized by emergence of global properties which are very difficult, if not impossible, to anticipate just from complete knowledge of component behaviors. Emergence, hierarchical organization and numerosity are some of the characteristics of complex systems. With increasing system complexity, achieving confidence in systems becomes increasingly difficult. With the recent trend towards significant footprint of complex system’s functionality being governed by machine learning based models and algorithms, there is a need to ensure that emergent behavior associated with such systems are well analyzed and understood. This presentation discusses an approach that involves developing machine learning classifier models that learns on potential negative and positive emergent behaviors. The machine learning model observes the various MOEs (Measures of Effectiveness) and MOPs (Measures of Performance) and learns the nature of emergent behavior. The approach is illustrated through two case studies – one at system level of an aircraft pitch controller, and another at system-of-system level of a swarm of UAV