Overcoming the Problem of Brittleness with the Metacognitive Loop.

摘要

The metacognitive loop (MCL) is architecture for automated noting, repairing, and learning from errors. Initial work on this award involved building special purpose MCL programs for each individual application domain Later in the award period a more uniform approach was designed that employs a general framework providing ontologies for types of Indications, Failures, and Repairs. This allows the past results in different domains to be achievable by a single MCL module, changing only the domain and the IFR ontologies. As a consequence, the investigators are now positioned (starting in 2009, with a new award) to begin to build a general-purpose MCL module that, when 'attached' to a given host program H and an initial set of IFR ontologies, can adapt to the domain that H lives in (and in the process adapt the ontologies to better fit that domain) so that H+MCL guides itself to become less brittle.