Antifragility for Intelligent Autonomous Systems
This addresses the challenge of enhancing the adaptability and self-improvement of goal-driven AI systems in unpredictable environments, though it appears incremental as it applies an existing concept to a specific domain.
The paper tackles the problem of making intelligent autonomous systems antifragile, meaning they improve when exposed to hazards, and demonstrates this with an AI-planning architecture that enables a wheeled robot to develop antifragile behavior in response to an oil spill.
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.