Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots

Understand

Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time.

• Assumptions about parts of the system made at design time may not hold at run time, especially when a system is deployed for long periods (e.g., over decades).
• Self-adaptation is designed to find reconfigurations of systems to handle such run-time inconsistencies.
• Planners can be used to find and enact optimal reconfigurations in such an evolving context.