Intent-Driven Modeling and Management of Complex Adaptive Systems – Proposed Approach and Research Agenda

Complex adaptive systems (CAS) have two defining characteristics. First, they are complex, i.e., composed of several interacting parts. Second, they are adaptive, i.e., their behavior can be changed in response to external stimuli and changes in the external environment. Due to this, managing such systems is quite challenging. Traditional approaches have involved defining policies that determine the behavior of any CAS under particular circumstances. However, such approaches are rigid and inflexible, since they are dependent on pre-specified policies. To that end, in this position paper, we describe an intent-driven approach to modeling and managing CAS. This would be a more flexible approach, not dependent on any specific policies, but which can be customized based on the context in which the CAS is functioning. We describe the various components of our approach, which include compositional reasoning to decompose the intent into sub-intents as per the context; mapping the sub-intents onto the execution model which will satisfy the intent; and feeding back the results of the execution to facilitate continual learning and continuous improvement in managing the CAS. In particular, one aspect that we highlight is the application of neurochaos learning, which uses chaos theory to facilitate rapid continual learning that would help improve the overall efficiency of our approach. For each component of our approach, we also present several research questions that need to be addressed before intent-driven management of CAS can become a reality.