Cognitive learning to counter security threats for kinematic actions in robots

摘要

A security control system for a kinematic robot uses a cognitive assessment agent to map proposed instructions to either legitimate or illegitimate actions based on contextual variables. The agent computes a security anomaly index score representing a variance of a likely kinematic action of the robot compared to acceptable actions. If the score exceeds a predetermined threshold, a security alert is generated for the robot's administrator. The contextual variables include a user profile, a user location, and subject matter of the kinematic actions. The analysis compares input text to predefined classification metadata, and can also compare verbal phrases or body gestures to corresponding baselines. Different numeric weights can be applied to the contextual variables. The computing begins with a default value for the score and thereafter increments or decrements the score based on the weights. The weights can be adjusted based on a supervisory appraisal of the computed score.