Adding metacognition to such systems can improve their operation in the face of perturbations. The Metacognitive Loop (MCL) works with a host system, monitoring its sensors and expectations. When a failure is indicated, MCL advises the host system on corrective actions.
Past implementations of MCL have been hand crafted and tightly integrated into their host systems. MCL is being reengineered to provide a C language API and to do Bayesian inference over a set of indication, failure, and response ontologies. These changes will allow MCL to be used with a wide variety of systems.
To prevent brittleness within MCL itself several items need to be addressed. MCL must be able to resolve host system failures when there is more than one indication of the failure or when a second indication occurs while MCL is attempting to help the host system recover from the failure. MCL also needs the ability of MCL to monitor itself and improve its own operation.
A twenty month plan is proposed to enhance MCL as described and to measure
(1) the effectiveness of MCL in improving the operation of the host
system; (2) MCL's operational efficiency in terms of additional
computational resources required and (3) the effort needed to incorporate
MCL into the host system.
Paper (pdf)
Slides (pdf)