Accession Number:

AD1084337

Title:

Benefits of SEI-CMU Collaboration regarding Use of Causal Learning

Descriptive Note:

Technical Report

Corporate Author:

Carnegie Mellon University Software Engineering Institute Pittsburgh United States

Personal Author(s):

Report Date:

2018-01-01

Pagination or Media Count:

9.0

Abstract:

Contents include Context of SCOPE Research Initial SCOPE Causal Search Results Benefits of CMU Collaboration Lessons Learned from CMU Collaboration Moderate Future Causal Learning for Simulation and Test Moderate Future Causal Learning for Sustainment Long Term Future Causal Learning Examples.

Subject Categories:

  • Cybernetics
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE