Runtime management and quantitative evaluation of changing system goals in complex autonomous systems

A key challenge in cyber-physical systems (CPS) design is their highly dynamic nature including runtime changes of system goals. Additional safety regulations or changed priorities may apply, e.g. (temporarily) focusing on safety goals after some incident occurred. Goal-aware CPS continuously evaluate goal achievement and autonomously perform adaptations for re-achievement at runtime. For complex system goals capturing dependencies, priorities, and conflicts, efficient goal evaluation techniques are required. To enable a fine-grained balancing of the cost-benefit ratio of autonomous decisions at runtime, a qualitative evaluation of goals is not sufficient. We provide an algorithm that efficiently calculates the quantitative “distance” between a system state and the system goals. We organise various goal types, their parent-children-relationships, context-dependent importances, and dependency relations in a hierarchical goal model. Due to its modular structure, goals can easily be added, removed, and changed at runtime. We illustrate our approach with an exemplary autonomous air drone delivery system and discuss it based on illustrative example scenarios. We argue that our approach enables a) the design of complex context-dependent quantitative goal models for autonomous goal-aware systems, b) the measurement of the impact of autonomous decisions at runtime, and c) the efficient runtime management of changing system goals.