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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. Training Systems  Decision Support LSI to assess IMINT text reports

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Strategic Research Directions in Artificial Intelligence

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  1. Strategic Research Directions in Artificial Intelligence AFRL/IF Workshop 26-27 June 2003 Ithaca, NY Jared Freeman, Ph.D. Principal Cognitive Scientist Aptima

  2. 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…

  3. 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?

  4. 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

  5. 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)

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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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