Home Home
Menu
  • Research
    • Artificial Intelligence
    • Automated Planning and Intent Recognition
    • Computational Social Science
    • Cybersecurity
    • Health and Telemedicine
    • Human Performance and UX Innovations
    • Linguistic Analysis
    • Playbook
  • Demonstrations
  • Publications
  • News
  • Staff
  • Contact Us
    • Careers at SIFT
    • SIFT Minneapolis
    • SIFT Boston

Search form

You are here

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, 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

  • 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

Primary Contacts

  • Robert Goldman
  • David Musliner

Researchers

  • Noam Benkler
  • Dan Bryce
  • Mark Burstein
  • Matthew DeHaven
  • Eric Engstrom
  • Richard Freedman
  • Scott Friedman
  • Harry Funk (he/him)
  • Christopher Geib
  • Robert Goldman

Pages

  • 1
  • 2
  • 3
  • >
  • >>>

Publications

  • Discovering Meaningful Labelings for RTS Game Replays via Replay Embeddings
  • Combinatory Categorial Grammar Learning for Plan Recognition in Domains with Type Trees
  • Extending Biology Models with Deep NLP over Scientific Articles
  • Embedding planning technology into satellite systems
  • Maintaining Evolving Domain Models

Pages

  • 1
  • 2
  • 3
  • >
  • >>>
Copyright © 2021, Smart Information Flow Technologies || Site Credits || Contact