  Resilience describes a system's ability to function under disturbances and
threats. Many critical infrastructures, including smart grids and
transportation networks, are large-scale complex systems consisting of many
interdependent subsystems. Decentralized architecture becomes a key resilience
design paradigm for large-scale systems. In this book chapter, we present a
multi-agent system (MAS) framework for distributed large-scale control systems
and discuss the role of MAS learning in resiliency. This chapter introduces the
creation of an artificial intelligence (AI) stack in the MAS to provide
computational intelligence for subsystems to detect, respond, and recover. We
discuss the application of learning methods at the cyber and physical layers of
the system. The discussions focus on distributed learning algorithms for
subsystems to respond to each other, and game-theoretic learning for them to
respond to disturbances and adversarial behaviors. The book chapter presents a
case study of distributed renewable energy systems to elaborate on the MAS
architecture and its interface with the AI stack.
