Software quality: Quality assessment for AI-based systems (see also 4.1.1 and 4.3.1.4)
This document describes the framework of the big data reference architecture and the process for how a user of the document can apply it to their particular problem domain.
The purpose of the standard is to extend the CORA ontology to represent more specific concepts and axioms that are commonly used in Autonomous Robotics. The extended ontology specifies the domain knowledge needed to build autonomous systems comprised of robots that can operate in all classes of unstructured environments. The standard provides a unified way of representing Autonomous Robotics system architectures across different R&A domains, including, but not limited to, aerial, ground, surface, underwater, and space robots. This allows unambiguous identification of the basic hardware and software components necessary to provide a robot, or a group of robots, with autonomy (i.e. endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance).
This standard is a logical extension to IEEE 1872-2015 Standard for Ontologies for Robotics and Automation. The standard extends the CORA ontology by defining additional ontologies appropriate for Autonomous Robotics (AuR) relating to: 1) The core design patterns specific to AuR in common R&A sub-domains; 2) General ontological concepts and domain-specific axioms for AuR; and 3) General use cases and/or case studies for AuR.
The goal is to have a technical survey for mitigating against threats introduced by adopting AI into systems. The technical survey shed light on available methods of securing AI-based systems by mitigating against known or potential security threats. It also addresses security capabilities, challenges, and limitations when adopting mitigation for AI-based systems in certain potential use cases
