DMAI4DA 2020
Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools, and Risks 2020

Table of Contents

• Organizers' Summary
  

Invited Talks

• XAI and Beyond (no paper included) 1
  Eric Vorm
• Scalable Machine Learning, Evolutionary Algorithms, and Frameworks (no paper included) 2
  Una-May O'Reilly, Erik Hemberg
• Soar and Beyond (no paper included) 3
  Mike van Lent
• The Holistic Battlespace: Why the Key to Resilience for AI/ML Algorithms is to Leverage Complexity Science (Presentation slides) 4-50
  Joe Schaff

Session 1

• Multi-Agent Mission Planning with Reinforcement Learning 51-57
  Sean Soleyman , Deepak Khosla
• Backdoor Attacks in Sequential Decision-Making Agents 58-65
  Zhaoyuan Yang , Naresh Iyer , Johan Reimann , Nurali Virani
• Using Lexical Link Analysis as a Tool to Improve Sustainment 66-68
  Edwin Stevens , Ying Zhao

Session 2

• Predictive Analytics in the Naval Maritime Domain 69-77
  Bonnie Johnson
• Parallel Algorithm for Approximating Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Strategic Planning 78-85
  Sam Ganzfried , Conner Laughlin , Charles Morefield
• Convolutional Neural Networks for Feature Extraction and Automated Target Recognition in Synthetic Aperture Radar Images 86-91
  John Geldmacher , Christopher Yerkes , Ying Zhao

Session 3

• On Interactive Machine Learning and the Potential of Cognitive Feedback (https://youtu.be/g3ypFFLX8jo) 92-99
  Chris Michael , Dina Acklin , Jaelle Scheuerman
• Evaluating Reinforcement Learning Algorithms for Evolving Military Games (https://youtu.be/VWIJifA5EXY) 100-104
  James Chao , Jonathan Sato , Crisrael Lucero , Doug Lange
• Causal Learning in Modeling Multi-segment War Game Leveraging Machine Intelligence with EVE Structures 105-112
  Ying Zhao , Bruce Nagy Tony Kendall Riqui Schwamm