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Office of the Deputy Under Secretary Of Defense for Readiness. Open Net-Centric Interoperability Standards for Training and Testing Metadata Working Group July 13, 2010. Carl Rosengrant Readiness and Training Policy, and Programs Office of the Deputy Under Secretary of Defense (Readiness)

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Open Net-Centric Interoperability Standards for Training and Testing Metadata Working Group


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    1. Office of the Deputy Under Secretary Of Defense for Readiness Open Net-Centric Interoperability Standards for Training and Testing Metadata Working Group July 13, 2010 Carl Rosengrant Readiness and Training Policy, and Programs Office of the Deputy Under Secretary of Defense (Readiness) Carl.rosengrant@osd.mil

    2. Outline • Introduction • What is ONISTT? • How is it being used? • Way forward

    3. What is ONISTT? • A framework for representing knowledge about: • Capabilities required to execute military tasks in various operational contexts (e.g., in Training Exercises, in Testing Events, or in real combat operations) • Resources - Real systems that can be assigned to play the various roles associated with executing those military tasks in the designated operational context and • An automated means for reasoning about that knowledge with the goal of Enabling agile interoperability

    4. ONISTT Concept in 1 Slide • “Lady Justice” is a symbolic metaphor for the process of implementing “blind justice” • The scales provides a means of assessing (weighing) the strength of the charges against the defendant versus the strength of arguments in his defense • The blindfold symbolizes the objectivity of the assessment, based only on the facts presented (not on the physical appearances of the plaintiff & defendant) • ONISTT is technology providing a means for automating Lady Justice • The issue being assessed is whether the capabilities available from a specified pool of resources (the supply) can satisfy the capabilities needed to perform a specified task (the demand) • The assessment function is performed by a general purpose application agnostic inference engine (not by procedural logic tailored for a specific domain) • The facts and logic (about the supply and the demand) are captured in Knowledge Bases (KBs) via a machine understandable language

    5. So…What can we do with an automated Lady Justice? Quite a bit, actually – The “matching supply to demand” paradigm is a rather profound concept that pervades many application domains…

    6. So… What Can You Do With an Automated Lady Justice? (Current Efforts) • Determining whether an improvised confederation of Live-Virtual/Constructive (L-VC) training systems can work harmoniously together to perform a mission (one that none were originally designed to support) Improvisational L-VC Interoperability • Determining whether an improvised confederation of testing resources can work harmoniously together to provide a complex test environment for a Net-centric System-Under-Test Analyzer for Net-centric System Confederations Project • Determining whether a candidate system design can satisfy its performance specification  Technical Data Package Example • Deriving a ruleset for downgrading information to allow flow from a higher security domain to a lower security domain • AF Research Lab (Rome NY) Task • Creating and compiling ontologies based on DIS/HLA/TENA/CTIA object models and publishing that knowledge to a repository • JFCOM Joint Composable Object Model Initiative • NATO working group is using ONISTT assets to demonstrate the feasibility of automating semantic interoperability in coalition operations • ONISTT NATO Initiative

    7. ONISTT: Ontology Development and Knowledge Capture Phase Tasks LVC Systems Training/Testing Infrastructures Training/Testing Environments Training/Testing Event Referents Training/Testing Resource Referents Task Knowledge Bases Resource and Domain Knowledge Bases Populate Knowledge Bases on the basis of ontologies and information in referents • Tasks • Roles • Capabilities needed • Task constraints • Resources • Capabilities • Domain knowledge 1a. Develop referents for training/testing events, tasks, and environments 1b. Develop referents for LVC systems, capabilities and quality metrics Ontologies ONISTT Concepts Develop ontologies to express pertinent characteristics of referents necessary for machine reasoning about interoperability General Domain Concepts DoD Domain Concepts Version 10-31-08

    8. Task Knowledge Bases Resource Knowledge Bases Deployment Knowledge Bases 1. Training/Testing Planner uses Knowledge Bases to • Define Taskplans • Propose candidate Confederation(s) (full or partial) • Resource pools • Confederations • Taskplans • Role assignments • Task constraints Analyzer Decision 3a. Return notification of failed verification. Back to Step 1 Verified Confederation(s) Configuration Artifacts ONISTT: Analyzer/Synthesizer Employment Phase 2. Analyzer/Synthesizer uses information in Knowledge Bases to • Assess given Confederation or b) Generate possible Confederations from Resource Pool 3b. Return verified Confederation(s) and Configuration Artifacts Version 11-01-08

    9. Referent and Ontology Description Languages • Referents • Natural language • UML (Unified Modeling Language) • Ontologies/Knowledge bases (KBs) • OWL (Web Ontology Language) • SWRL (Semantic Web Rule Language) • Bridging between UML and OWL • ONISTT team developed OWL “profile” for UML per Object Management Group (OMG) Ontology Definition Metamodel (ODM) • ONISTT team recommendations were adopted by the OMG as improvements to the ODM specification • Sandpiper tool

    10. ONISTT/ANSC Benefits • Facilitates composition of improvised confederations of agile systems • Discovers and verifies resource compositions tailored to a given purpose • Optimizes use of existing resources • Partially automating BOGSAT process--replaces ad hoc, trial & error approach • Exercise planners get rapid, reusable access to accumulated expert information and lessons learned • Avoid repeating costly mistakes • Repeat successes • Focuses spotlight on areas that are problematic for interoperability • Target them for improvement or standardization

    11. Outreach Accomplishments • International Semantic Web Conference (ISWC) 2009 peer-reviewed paper • “Reasoning about Resources and Hierarchical Tasks Using OWL and SWRL” • Winter Simulation Conference (WSC) 2009 invited paper • “Ontologies and Tools for Analyzing and Synthesizing LVC Confederations” • Expanded version for Journal of Simulation (forthcoming) • IEEE Policy 2010 peer-reviewed paper • “Policy-Based Data Downgrading: Toward A Semantic Framework and Automated Tools to Balance Need-To-Protect and Need-To-Share Policies” • www.onistt.org Website (Send e-mail to Mr. Rosengrant for access)

    12. Way Forward • Although demos conducted to date have shown the potential value of this technology, there remains considerable development to be accomplished in order to “operationalize it” • Widespread deployment of commercial applications in Semantic Web technology are facing many of the same challenges, accelerating work in the area • We anticipate benefiting from these developments • We are seeking to collaborate with other organizations who are working in ontologies, in order to leverage work that might be otherwise have to be duplicated!

    13. Questions?

    14. Backup

    15. Knowledge Capture Phase

    16. Analyzer/Synthesizer architecture

    17. ONISTT/ANSC Benefits • Flexible and reusable definition of test events and test objectives • Allow for subtasks nested to arbitrary depth • Ensure complete coverage of test objectives by test events • Automatically check for adequate coverage of test value sets (values of critical controlled variables) • Automated synthesis of a completed test plan • Generate valid refinements of given, partial plan • Automatically identify combinations of T&E resources best suited to attain objectives • Synthesis problem is more difficult than analyzing a fully specified test plan

    18. OntologyGroups Task plan ontologies JSAF Tomahawk launch vs JIMM/IADS, EA6B jamming IADS radar… Task ontologies Engagement, Sensing, Countermeasure, Communication, Appearance, Motion, Terrain… Resource ontologies JSAF, JIMM, JIMES, C3Driver, JSTARS, VSTARS, EA6B_WSSL, F/A-18 AWL, E2C ESTEL,Pt Mugu, Pax River, Ft Huachuca,Eglin, Edwards, China Lake, White Sands, InterTEC/JMETC comm infrastructure... … Capability ontologies Engagement, Sensing, Countermeasure, Communication, Appearance, Motion, Terrain… Domain ontologies Spatial reference frame, Entity, Quantity, Sensors, Time, Terrain, Networks, Comm architecture (Link-16, USMTF, TENA, DIS, OTH-Gold, VMF, IBS, Link-11, voice)…