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Strategic Research Directions in Artificial Intelligence. AFRL/IF Workshop 26-27 June 2003 Ithaca, NY Jared Freeman, Ph.D. Principal Cognitive Scientist Aptima. Aptima’s Work. Training Systems  Decision Support LSI to assess IMINT text reports

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strategic research directions in artificial intelligence

Strategic Research Directions in Artificial Intelligence

AFRL/IF Workshop

26-27 June 2003

Ithaca, NY

Jared Freeman, Ph.D.

Principal Cognitive Scientist

Aptima

aptima s work
Aptima’s Work
  • Training Systems  Decision Support
    • LSI to assess IMINT text reports
    • SVM + IBIS rationale models to assess PSYOPS plans
    • Cognitive models + Speech-to-text + NLP to train F-15 & AWACS
    • Speech-to-text + SVM to categorize MOUT comms
    • NLP + HF taxonomies to categorize usability reports
  • Formal Modeling of Organizations
    • Optimization + dynamic clustering to define smallest, fastest human team for a mission given a technology suite
    • Social network modeling of terrorist organizations
  • Military domain analysis
    • Intel, AOC, AWACS, Navy Air wing, urban warfare…
method
Method
  • Participants
    • Head researcher in an AF training lab
    • Chief of AF intelligence training
    • Leading researcher in design of organizational structure and process
    • Engineer heading DoD human-centered engineering firm
    • Cognitive scientist with 25 years experience in military systems development
    • Others…
  • Question
    • What directions in AI research will serve the AF over the next decade?
hot topics acquiring data for modeling
Hot Topics: Acquiring Data for Modeling
  • Title: Data mining for human / team models
  • Objective:
    • Use extant data to accelerate development & validation of behavioral and cognitive models
      • Simple: track id
      • Complex: PSYOPS planning
    • Develop and validate performance metrics
  • Challenges:
    • Data acquisition from proprietary, independent, classified systems
    • Data pre-processing
    • Hypothesis generation & testing
hot topics modeling decision makers
Hot Topics: Modeling Decision Makers
  • Title: Cognitive / Behavioral / Organizational Models
  • Objectives:
    • Increase efficiency w/ synthers (vs. humans) in
      • Large scale experiments, especially at low levels in organizations
      • Individual training of strategic skills (vs. procedural)
    • Analyze strategic expertise at high levels, e.g., JFACC (2-3 star)
    • Test new organizational architectures & processes (TTPs)
    • Test effects of technology insertion in these closed loop systems
    • Develop models of communicative & social behavior
  • Challenges:
    • Efficient development of models from top (theory) & bottom (data)
    • Modeling strategic (vs. procedural) skill
    • Modeling interaction of cognition  decision support
    • Learning from users (batch and real time)
hot topics modeling decision makers1
Hot Topics: Modeling Decision Makers
  • Title: Organizational management
  • Objectives:
    • Help teams adapt on the fly
      • Speed – when to accelerate
      • Tasking – what tasks to drop
      • Process – what process to adapt
      • Structure – when and how to adapt architecture
  • Challenges:
    • Dynamic organizational models
      • Organizational adaptation
      • Organizational evolution
    • Empirical validation
hot topics modeling content
Hot Topics: Modeling Content
  • Title: Multi-domain information fusion
  • Objectives:
    • Fuse hard intel (SIGINT) with soft intel (HUMINT, open source)
  • Challenges:
    • Knowledge acquisition re: organizations with multiple experts, multiple data sources
    • Modeling interaction of domain specialists
hot topics modeling content1
Hot Topics: Modeling Content
  • Title: Ontologies
  • Objectives:
    • Improve the psychological validity of ontologies
  • Challenges:
    • Human categorization is
      • Dynamic: Generative (Barselou)
      • Continuous: From prototypes (robins) to exceptions (penguins) (Rosch)
    • Ontologies as defined in computer science are
      • Static
      • Dichotomous
hot topics modeling reasoning
Hot Topics: Modeling Reasoning
  • Title: Explaining reasoning
  • Objectives:
    • Justify training assessments & diagnoses: feedback
    • Justify advice to varied specialists, e.g., explain ROE decisions to
      • AWACS Air Weapons Officer
      • AWACS lawyers: Airborne Cmd Element + JAG (in AOC)
  • Challenges:
    • Customize language per user or level of expertise
    • Customize granularity or style of reasoning (psychological validity per paradigm, per level of expertise)
    • Ascertaining when explanations are weak, models are wrong
hot topics modeling reasoning1
Hot Topics: Modeling Reasoning
  • Title: Supporting reasoning (vs. replacing it)
  • Objectives:
    • “The greatest benefit AI can bring to me … is the ability to know which question to ask.”
    • “The one thing that I note is the glaring emphasis on replacing the "man in the loop" almost to the exclusion of providing performance support…there will always have to be a man in the loop. the AI capability is only an assist…not the answer.”
  • Challenges:
    • Modeling of question asking, critical thinking
    • Dialogue modeling
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