A Framework for Evidence-Based Licensure of Adaptive Autonomous Systems
Abstract
Adaptive autonomous systems of interest to DoD have great potential to complement human performance in a wide range of missions. This particularly is true for adaptive systems that learnthose whose behavior on a given set of inputs may change over time, even after the system has been fielded. Such adaptation, however, makes exhaustive testing, certification, and licensure of the final system impossible. The challenge is to establish high confidence that the system will perform dependably and behave as intended while safely, securely, reliably, and effectively carrying out the assigned missions. This paper adapts approaches from other disciplines, such as software assurance theory, where safety as well as performance is paramount. The key features of the approach include taking and retaining performance data from the beginning of development, establishing a formal means to assess actual performance compared with desired performance throughout the development, and recognizing that system testing will need to continue beyond a fielding decision. An additional complexity is that performance testing needs to address how the autonomous modules make decisions and provide a standard for doing that. That is, test, evaluation, verification, and validation of such systems must inform testers about how a system satisfies requirements during development and about its potential to make effective system changes after fielding.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2016
- Accession Number
- AD1014101
Entities
People
- Christopher A. Martin
- David A. Sparrow
- David M. Tate
- Franklin L. Moses
- Rebecca A. Grier
Organizations
- Institute for Defense Analyses