Open AccessA Self‐Help Guide for Autonomous Systems
Author(s) -
Anderson Michael L.,
Fults Scott,
Josyula Darsana P.,
Oates Tim,
Perlis Don,
Schmill Matt,
Wilson Shomir,
Wright Dean
Publication year - 2008
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v29i2.2126
Subject(s) - notice , literal (mathematical logic) , computer science , domain (mathematical analysis) , artificial intelligence , cognitive science , psychology , programming language , political science , mathematics , law , mathematical analysis
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 metacognitive 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.