A Factor-Based Framework for Decision-Making Competency Self-Assessment
This addresses the need for robots to self-assess their functional abilities in decision-making, though it appears incremental as it builds on early niche applications.
The paper tackles the problem of generating human-understandable competency self-assessments for robots by developing a Factorized Machine Self-Confidence framework that combines probabilistic meta-reasoning for planning under uncertainty, resulting in a novel set of generalizable self-confidence factors applicable to diverse problems.
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.