  We summarize our efforts to date in developing a framework for generating
succinct human-understandable competency self-assessments in terms of machine
self confidence, i.e. a robot's self-trust in its functional abilities to
accomplish assigned tasks. Whereas early work explored machine self-confidence
in ad hoc ways for niche applications, our Factorized Machine Self-Confidence
framework introduces and combines several aspects of probabilistic meta
reasoning for algorithmic planning and decision-making under uncertainty to
arrive at a novel set of generalizable self-confidence factors, which can
support competency assessment for a wide variety of problems.
