# Antifragility for Intelligent Autonomous Systems

## TL;DR

This paper introduces an architecture that enables goal-driven autonomous systems to become antifragile, improving their capabilities by leveraging hazards and strategic deliberation, demonstrated through a robot case study.

## Contribution

It presents a novel architecture that imparts antifragility to AI-planning autonomous systems, allowing them to self-improve through hazards and deliberation.

## Key findings

- The robot developed antifragile behavior in response to oil spill hazards.
- The architecture enables self-improvement by uncovering new capabilities.
- Antifragility was successfully demonstrated in a case study.

## Abstract

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.