Modeling Emergent Behaviour for Enhanced Autonomy in Cyber-Physical Systems

The convergence of Cyber-Physical Systems (CPS) and autonomous systems presents numerous application areas and challenges. Many CPS use cases require autonomy, which necessitates robust monitoring, maintenance, and longevity of these systems. However, handling unmodeled data, environmental uncertainties, and emergent behaviour poses significant challenges. This paper proposes the use of a novel formalism, Extended Hidden Markov Models with \(\epsilon \) -emissions ( \(\epsilon \) -HMMT), to estimate the correctness of an autonomous system’s emergent behaviour in complex and changing environments. We discuss the advantages and disadvantages of different modeling approaches, emphasizing the need for a more adaptable and robust modeling technique.