Paper Info
Title
Antifragility for Intelligent Autonomous Systems.
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.
Year
Venue
Field
2018
arXiv: Artificial Intelligence
Deliberation,Oil spill,Architecture,Computer science,Risk analysis (engineering),Artificial intelligence,Autonomous system (Internet),Robot,Machine learning,Automated planning and scheduling
DocType
Volume
Citations 
Journal
abs/1802.09159
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Anusha Mujumdar121.44
Swarup Kumar Mohalik285.33
Badrinath Ramamurthy3234.19