Antifragility for Intelligent Autonomous Systems
  Antifragile systems grow measurably better in the presence of hazards. This
is in contrast to fragile systems which break down in the presence of hazards,
robust systems that tolerate hazards up to a certain degree, and resilient
systems that -- like self-healing systems -- revert to their earlier expected
behavior after a period of convalescence. The notion of antifragility was
introduced by Taleb for economics systems, but its applicability has been
illustrated in biological and engineering domains as well. In this paper, we
propose an architecture that imparts antifragility to intelligent autonomous
systems, specifically those that are goal-driven and based on AI-planning. We
argue that this architecture allows the system to self-improve by uncovering
new capabilities obtained either through the hazards themselves (opportunistic)
or through deliberation (strategic). An AI planning-based case study of an
autonomous wheeled robot is presented. We show that with the proposed
architecture, the robot develops antifragile behaviour with respect to an oil
spill hazard.
