Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks
Papers from the AAAI Fall Symposium
Ying Zhao, Chair
Technical Report FS-17-03
Published by The AAAI Press, Palo Alto, California.
This technical report has been published as a section in The 2017 AAAI Fall Symposium Series: Technical Reports.
Contents
Position Paper: Rational Behavior Model (RBM) and Human-Robot Ethical Constraints Using Mission Execution Ontology (MEO) / 192
Don Brutzman, Curtis Blais, Robert McGhee, Duane Davis
Position Paper: Towards a Repeated Bayesian Stackelberg Game Model for Robustness Against Adversarial Learning / 194
Prithviraj Dasgupta, Joseph Collins
Integration of Graphs and Representation Learning / 196
Arjuna Flenner
Phylogenetic-Inspired Probabilistic Model Abstraction in Detection of Malware Families / 200
Krishnendu Ghosh, Jeffery Mills, Joseph Dorr
Using D3 to Visualize Lexical Link Analysis (LLA) and ADS-B Data / 206
Quinn Halpin, Ying Zhao, Anthony Kendall
Autonomous Outcomes: Shaping the Future Data Environment to Build Trust in Artificial Intelligence and Machine Learning Applications / 210
Scott A. Humr
A Systems Approach to Battle Management Aids / 212
Bonnie Johnson
Simple Object Classification Using Binary Data / 218
Deanna Needell, Rayan Saab, Tina Woolf
Analysis of Automatic Dependent Surveillance-Broadcast Data / 225
Ryan Salcido, Anthony Kendall, Ying Zhao
A Framework Using Machine Vision and Deep Reinforcement Learning for Self-Learning Moving Objects in a Virtual Environment / 231
Richard Wu, Ying Zhao, Alan Clarke, Anthony Kendall