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Regulating the Exchange of Tactical Information Using the KAoS Policy Services Framework Larry Bunch Florida Institute for Human and Machine Cognition IHMC : Jeff Bradshaw (PI), Matt Johnson, James Lott, Paul Feltovich, Niranjan Suri, Marco Carvalho

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  1. Regulating the Exchange of Tactical Information Using the KAoS Policy Services FrameworkLarry Bunch Florida Institute for Human and Machine Cognition IHMC: Jeff Bradshaw (PI), Matt Johnson, James Lott, Paul Feltovich, Niranjan Suri, Marco Carvalho ARL CISD: Larry Tokarcik, Robert Winkler, Somiya Metu July 22, 2009

  2. Objectives • Facilitate secure automated information sharing in net-centric environments • Heterogeneous (e.g. coalition forces) • Tactical (e.g. MANET) • Through a framework for regulating information sharing • Rich language to associate information sharing contexts with requirements • Automated control and enforcement capabilities • Maintain human oversight and approval

  3. Policy Focus More flexible and open information exchange depends upon • Semantically-rich policy representations • Specify the kinds of information that can be shared and with whom • Identify operational contexts that impact information sharing • Easily extend to include new domains and concepts • Advanced policy reasoning capabilities • Context matching • Spatial reasoning • Temporal reasoning • Advanced policy enforcement capabilities • automatically filter information • abstract and transform information • maintain appropriate levels of human oversight and approval

  4. Policy Representation • Rich and meaningful • Describe contexts in human-accessible terms involving multiple attributes at multiple levels of abstraction • Formal • Support automated reasoning and enforcement • Flexible and Extensible • Quickly adapt to changing needs and contexts W3C standard Web Ontology Language (OWL) extended with Role-Value-Map ‘variables’ and enhanced reasoning capabilities

  5. Information Sharing Policy

  6. Policy Representation • Easy to use graphical tools • Policy templates and wizards • Hypertext policy definition language

  7. Policy Representation

  8. Policy Representation • Support for obligations as well as authorizations • Transform & Redact • Prioritize & Delay • Notify & Share • Obtain human approval • Support for sophisticated context descriptions • Actions • Actors • Attributes • States • History

  9. Policy Reasoning • Context matching • Deontic logic using description logic to classify actions and context attributes at multiple levels of abstraction • Intensional and extensional group membership • Role and team assignments • Spatial reasoning • Location (e.g. within an area of operations) • Proximity (e.g. unit to SOF) • Temporal reasoning • Relationships among actions

  10. Policy Enforcement • Application • Policy-aware systems interpret and apply policy to modify their behavior • Middleware • Enforcement components are dynamically instantiated by middleware to apply policy without the knowledge and cooperation of the affected applications

  11. Policy Enforcement Topologies

  12. Blue Force Tracking Demonstration • Policy-based control over the symbols shared among coalition forces • Domain (US Class., US Unclass., UK, NGO) • MIL-STD-2525b Symbol • Affiliation, Echelon, Status, Country • Warfighting Symbols, Tactical Graphics ... • Spatial Reasoning • Agile Computing Middleware enforcement

  13. Blue Force Tracking Demonstration

  14. MIL-STD-2525b Ontology

  15. Blue Force Tracking Demonstration • Symbol abstraction policy • SOF warfighting symbol abstracted to No Fire Zone tactical graphic for US unclassified domains • Proximity-based exception policy • SOF symbol revealed to US forces when within N meters of US unclassified forces • Middleware enforces policies by dynamically instantiating transformation and filtering components

  16. Blue Force Tracking: Abstraction

  17. Blue Force Tracking: Proximity

  18. Unattended Sensor Data Harvesting Demonstration • MANET environment • Policy control of the Agile Computing Dissemination Service to independently regulate • Replication of data by the middleware • Prioritize what data is replicated based on mission • Prevent/Permit a network node to be a carrier • Clients’ ability to subscribe, send, and receive data • Prevent/Permit based on metadata (e.g. type, source, classification level) • Transform, redact, notify, approve • Sensor alerting based on prior alert patterns • A1 followed by A2 within 5 min. followed by A4 => Low Priority

  19. Unattended Sensor Data Harvesting Demonstration

  20. Harvesting Demonstration

  21. Transition • ARL CISD, Adelphi: in-house development of unattended sensor signaling policies • ARL CISD, Aberdeen: intelligence analyst support tool • CERDEC: COBRA and THINK ATO’s

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