  Modern civilian and military systems have created a demand for sophisticated
intelligent autonomous machines capable of operating in uncertain dynamic
environments. Such systems are realizable thanks in large part to major
advances in perception and decision-making techniques, which in turn have been
propelled forward by modern machine learning tools. However, these newer forms
of intelligent autonomy raise questions about when/how communication of the
operational intent and assessments of actual vs. supposed capabilities of
autonomous agents impact overall performance. This symposium examines the
possibilities for enabling intelligent autonomous systems to self-assess and
communicate their ability to effectively execute assigned tasks, as well as
reason about the overall limits of their competencies and maintain operability
within those limits. The symposium brings together researchers working in this
burgeoning area of research to share lessons learned, identify major
theoretical and practical challenges encountered so far, and potential avenues
for future research and real-world applications.
