Deciding Not to Decide Using Resource-Bounded Sensing
General Robotics, Automation, Sensing and Perception Laboratory
We view the problem of sensor-based decision-making in terms of two components: a sensor fusion component that isolates a set of models consistent with observed data, and an evaluation component that uses this information and task-related information to make model-based decisions. In previous work we have described a procedure for computing the solution set of parametric equations describing a sensor-object imaging relationship, and also discussed the use of task-specific information to support set-based decision-making methods. In this paper, we investigate the implications of allowing one of the decision-making options to be "no decision," whereupon a human might be called to aid or interact with the system. In particular, this type of capability supports the construction of supervised or partially autonomous systems. We discuss how such situations might arise and give concrete examples of how a system might reach such a decision using our techniques.