Humans learn from their mistakes. When things go badly, we notice that something is amiss, figure out what went wrong and why, and attempt to repair the problem. Artificial systems depend on their human designers to program in responses to every eventuality and therefore typically don’t even notice when things go wrong, following their programming over the proverbial, and in some cases literal, cliff. This article describes our past and current work on the Meta-Cognitive Loop, a domain-general approach to giving artificial systems the ability to notice, assess, and repair problems. The goal is to make artificial systems more robust and less dependent on their human designers.
['Michael L. Anderson', 'Scott Fults', 'Darsana P. Josyula', 'Tim Oates', 'Donald Perlis', 'Shomir Wilson', 'Dean Wright']
A Self-Help Guide for Autonomous Systems