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Home » Research » Artificial Intelligence

Artificial Intelligence

SIFT has gathered extensive experience in multiple areas in the field of artificial intelligence, including transfer/reinforcement/deep learning, neurosymbolic systems, automated planning, intent recognition and natural language processing. Many of our projects can be characterized as employing both AI techniques and other, more domain specific approaches.

Research Evolution

  • CHA-CHA

    Characterizing Human Activities for Cancer Health Awareness
  • SCORE

    Systematizing Confidence in Open Research and Evidence
  • ASIST

    Artificial Social Intelligence for Successful Teams (ASIST)
  • A-Team

    Army Advanced Teaming
  • ACUMEN

    Analyzing Cultural Motif Effects in Networks
  • M-PRIMUS

    Mission Planning for Resources in Integrated Mixed Undersea Systems
  • CPS

    Creative Problem Solver
  • SD2

    Synergistic Discovery and Design
  • REPAIR

    Resilient Emergent Properties for Autonomous Agent InteRactions
  • DistrO

    Distributed Operations
  • FuzzBomb

    FuzzBomb
  • CLiC

    Communicating in Language-Integrated Context
  • R3

    Reading, Reasoning, and Reporting
  • Marshal

    Marshal
  • STRIDER

    Semantic Targeting of Relevant Individuals, Dispositions, Events, and Relations
  • FuzzBuster

    FuzzBuster
  • HAMMER

    Highly Autonomous Mission Manager for Event Response
  • SAFE-P

    System for Assurance of Flight Executable Procedures
  • Kulit

    A Workflow for High Resolution Communication
  • Deep Green

    Deep Green
  • YAPPR

    Yet Another Probabilistic Plan Recognizer
  • IL

    Integrated Learning

Researchers

  • Robert Goldman
  • Jeremy Gottlieb
  • Josh Hamell
  • Steven Johnston
  • Pete Keller
  • Ugur Kuter
  • Jack Ladwig
  • David McDonald
  • Christopher Miller
  • Joseph B. Mueller

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Publications

  • Self-Adaptation Metrics for Active Cybersecurity
  • Meta-control for Adaptative Cybersecurity in FUZZBUSTER
  • Automated Fault Analysis and Filter Generation for Adaptive Cybersecurity
  • Improving Automated Cybersecurity by Generalizing Faults and Quantifying Patch Performance
  • Chronomorphic Programs: Using Runtime Diversity to Prevent Code Reuse Attacks

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