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Accession Number:
AD1084337
Title:
Benefits of SEI-CMU Collaboration regarding Use of Causal Learning
Corporate Author:
Carnegie Mellon University Software Engineering Institute Pittsburgh United States
Report Date:
2018-01-01
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.
Descriptive Note:
Technical Report
Pages:
0009
Distribution Statement:
Approved For Public Release;
Contract Number:
FA8702-15-D-0002
File Size:
0.74MB
