Lifelong Testing of Smart Autonomous Systems by Shepherding a Swarm of
  Watchdog Artificial Intelligence Agents
  Artificial Intelligence (AI) technologies could be broadly categorised into
Analytics and Autonomy. Analytics focuses on algorithms offering perception,
comprehension, and projection of knowledge gleaned from sensorial data.
Autonomy revolves around decision making, and influencing and shaping the
environment through action production. A smart autonomous system (SAS) combines
analytics and autonomy to understand, learn, decide and act autonomously. To be
useful, SAS must be trusted and that requires testing. Lifelong learning of a
SAS compounds the testing process. In the remote chance that it is possible to
fully test and certify the system pre-release, which is theoretically an
undecidable problem, it is near impossible to predict the future behaviours
that these systems, alone or collectively, will exhibit. While it may be
feasible to severely restrict such systems\textquoteright \ learning abilities
to limit the potential unpredictability of their behaviours, an undesirable
consequence may be severely limiting their utility. In this paper, we propose
the architecture for a watchdog AI (WAI) agent dedicated to lifelong functional
testing of SAS. We further propose system specifications including a level of
abstraction whereby humans shepherd a swarm of WAI agents to oversee an
ecosystem made of humans and SAS. The discussion extends to the challenges,
pros, and cons of the proposed concept.
