Computational Mechanisms to Support Reporting of Self Confidence of Automated/Autonomous Systems

This paper describes a new candidate method of computing autonomous "self confidence." We describe how to analyze a plan for possible but unexpected break down cases and how to adapt the plan to circumvent those conditions. We view the result plan as more stable than the original one. The ability of achieving such plan stability is the core of how we propose to compute a system’s self confidence in its decisions and plans. This paper summarizes this approach and presents a preliminary evaluation that shows our approach is promising.