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1 Failures in C2 Technology Why command and control has stagnated Doug Dyer April 03 Joint Vision 2010 provides an operationally based template for the.

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Presentation on theme: "1 Failures in C2 Technology Why command and control has stagnated Doug Dyer April 03 Joint Vision 2010 provides an operationally based template for the."— Presentation transcript:

1 1 Failures in C2 Technology Why command and control has stagnated Doug Dyer April 03 Joint Vision 2010 provides an operationally based template for the evolution the Armed Forces for a challenging and uncertain future. … This vision of future warfighting embodies the improved intelligence and command and control available in the information age...” 1997: Joint Vision 2010

2 2 Overview  Why haven’t past technology efforts succeeded in improving C2? How can current and future efforts (DJC2, JC2, SDE) do better?  Structured information What it is How to develop, integrate, extend, and sustain it Reasons to believe we’ll succeed  Creating Structured Information with 5 th Generation Applications Bottom-up, inside-out, do-it-yourself Applications: workflow, structured , interfaces to problem solvers, data editing Applications as creators and reasons to sustain structured data

3 3 Previous programs correctly identified goals Example: Joint Vision 2010 The basis for this framework is found in the improved command, control, and intelligence which can be assured by information superiority… … These transformations will be so powerful that they become, in effect, new operational concepts:  Dominant maneuver  Precision engagement  Full-dimensional protection  Focused logistics. -- John M. Shalikashvili, Chairman of the Joint Chiefs of Staff

4 4 Previous programs correctly identified goals Example: Joint Vision 2010 From the Air Force 1998 TAP: GLOBAL AWARENESS Consistent Battlespace Knowledge Precision Information Global Information Base GLOBAL INFORMATION EXCHANGE Distributive Information Infrastructure Universal Transaction Services Assurance of Service Global Connectivity to Aerospace Forces DYNAMIC PLANNING/EXECUTION Predictive Planning and Preemption Integrated Force Management and Execution Execution of Time Critical Missions/Real Time Sensor-to-Shooter Operations Joint, Combined and Coalition Operations All of these goals are appropriate, but we’ve only made progress on the service infrastructure

5 5 Previous programs correctly identified goals Example: Joint Vision 2010 Information Architecture A little optimistic about the Common Operational Picture Otherwise, good architecture “When combined with extensive coverage of the order of battle of the opposing forces, faster than real time simulation of potential enemy courses of action, and exceptional high capacity communications, military commanders in 2010 will see the three domains pulled ever closer together. The outcome of this process should be a command and control system that bases its decisions and management actions on a bedrock of accurate and common understanding and acts (reacts also, but the initial responses of commanders that may be the most important could be those that are proactive) to make quality and timely decisions. Further, the quality of the information allows us to better parse the decision making process. Simple Decisions are those for which a stimulus or event requires a specific response. These are easily automated. Contingencies can be considered in advance and Contingent Decisions are those that require a response from a known list and can be categorized through "if-then" logic. These are also quite automatable. On the other hand, Complex Decisions are those in which the decision-maker must create the list and these are extremely difficult, if not impossible, to automate. Automatable support (e.g., M&S) can nevertheless assist the decision-maker in complex decision-making.” --- EBRI paper

6 6 Previous programs correctly identified goals Example: Joint Vision 2010 Information Architecture A little optimistic about the Common Operational Picture Otherwise, good architecture “When combined with extensive coverage of the order of battle of the opposing forces, faster than real time simulation of potential enemy courses of action, and exceptional high capacity communications, military commanders in 2010 will see the three domains [Battlespace Awareness, Decision-Making, and Battle Management]pulled ever closer together. The outcome of this process should be a command and control system that bases its decisions and management actions on a bedrock of accurate and common understanding and acts (reacts also, but the initial responses of commanders that may be the most important could be those that are proactive) to make quality and timely decisions. Further, the quality of the information allows us to better parse the decision making process. Simple Decisions are those for which a stimulus or event requires a specific response. These are easily automated. Contingencies can be considered in advance and Contingent Decisions are those that require a response from a known list and can be categorized through "if-then" logic. These are also quite automatable. On the other hand, Complex Decisions are those in which the decision-maker must create the list and these are extremely difficult, if not impossible, to automate. Automatable support (e.g., M&S) can nevertheless assist the decision-maker in complex decision-making.” --- EBRI paper

7 7 So, what’s the problem? Answer #1: Failure to build an enduring structured data model

8 8 Structured information is the foundation for new C2 capability Structured Data Model Tailored Feeds Sentinels Standard Presentation Formats Rapid situation understanding Assessment of coverage Rules Simulations Constraint checking Case-based reasoning Generative Planning Dialog & machine learning Web Services Procedures In general, any technology intended for decision support requires structured information: variables and values in context Once you have a structured data model of the battlespace, you can stack up an impressive array of technologies resulting in new command and control capability Effects:  Reduced workload  Better situation awareness  Easier coordination  Better decisions faster  Increased span of control

9 9 Joint Vision 2010 Plan and Situation Data Models Again, JV2010 had the right idea… But JV2010 never successfully implemented the models… Without a structured data model, you can’t get smart algorithms It makes sense to figure out why past attempts failed and how to do it better in the future No other C2 effort has created an enduring data model

10 10 So, what’s the problem? Answer #2: Failure to define, create, and sustain smart algorithms

11 11 Smart algorithms are the keys to decision speed and quality Assumed: future operations will be more complex Structured Data Model Tailored Feeds Sentinels Standard Presentation Formats Rapid situation understanding Assessment of coverage Rules Simulations Constraint checking Case-based reasoning Generative Planning Dialog & machine learning Web Services Procedures Some might disagree based on past efforts, but machine-amplified brain-power has the best potential to make better decisions faster… for getting agile How? Tons of ways…  Creating information in common formats  Identify missing information  Tailoring information to people’s role  Timely alerts  Workflow and coordination  Implication calculation  Resource allocation and optimization  Planning and goal satisfaction  Hypothesis generation  Possible futures  Problem-solving  Process improvement  Adaptation of our knowledge base

12 12 A Joint Vision 2010 Functions and Algorithms Again, JV2010 had the right idea… But even JV2010 demos couldn’t meet the vision Without smart algorithms, you can’t get intelligent assistance from your computer It makes sense to figure out why past attempts failed and how to do it better in the future No other C2 effort has fielded smart, large-scale automation

13 13 Defining Structured Information

14 14 “Structure” as defined by Webster’s Dictionary Two defining characteristics…  A number of parts… i.e., an enumerated list of things (known; not infinite)  … that are put together in a specific way Example: An object in an object-oriented computer language Has a number of attributes or variables associated with it Is bound by a set of defined relationships Interacts with other objects via a specific set of interfaces

15 15 Structured Information for Command and Control Defining an “Information Element” ContextVariableValueMeta-Data For OPLAN 3400, Operation Bullfrog, root branch, according to planning by Cmdr Newton for SEAL Team 4’s ingress plan… The ingress resources required are: 2 Mark-V Assault Boats As decided at 12:04:13 Z on 16-Mar-03 using a default rule which was apparently accepted by the user Example: An information element is an atomic unit of structured information It includes:

16 16 Structured Information Defining an “Information Element” ContextVariableValueMeta-Data For OPLAN 3400, Operation Bullfrog, root branch, according to planning by Cmdr Newton, for SEAL Team 4’s ingress plan… The ingress resources required are: 2 Mark-V Assault Boats As decided at 12:04:13 Z on 16-Mar-03 using a default rule which was apparently accepted by the user Example: An information element is an atomic unit of structured information It includes: Plans and situations can be partially described by sets of variables and values Context dictates which variables belong together Meta-Data includes other useful information

17 17 A structured data model represents plans and situations SDM: a set of information elements Structured Data Model ContextVariableValueMeta-Data C1 C3 C2 C4 C1 C2 C4 C11 C22 C1 C4 … V1 V2 V1 V3 V7 V2 V3 V6 V4 V1 V2 V3 … Val A Val B Val A Val C Val D Val F Val X Val U Val T Val G Val E Val M … Meta-data {a, b, d} Meta-data {l, k, c} Meta-data {q, j, v} Meta-data {t, w, i} Meta-data {o, p, r} Meta-data {g, h, g} Meta-data {k, u, u} Meta-data {z, p, d} Meta-data {a, v, e} Meta-data {p, c, y} Meta-data {i, y, d} Meta-data {s, w, a} … The set of variables associated with any particular plan or situation are found by matching on context

18 18 How to build a structured data model

19 19 Building and Exploiting a Structured Data Model Structured Data Model A way to build it Incentives Resources Approach Technology A way to introduce and integrate it Workflows Briefings Planning A way to update and improve it Dynamic state change Process improvement Requirements for it Manual search for information Decision support Bandwidth limitations Multi-level security Neat things you can do with it Format to speed understanding Heuristics automate details Decision logic to aid reasoning Deep analysis for hard problems

20 20 Two ways to build a structured data model Traditional approach: Top Down  Developers use knowledge engineering to learn the domain  Create models that cover the domain  Ontology  Database schema  XML definitions  Frequent patches to get coverage, correct errors  … ~3-18 months  Develop applications … ~ 6 months  Integrate applications into users processes... ~3 months  Adapt applications as user processes improve… ~3 months  Process ends when funding runs out An alternative approach: Bottom Up  Developers demonstrate tools, but users extend examples and build new applications  Create forms for a specific purpose  Users pick the terms and define important relationships  Forms define micro-ontology, namespace  Database schema created automatically  XML may be projected  … ~1 day  Forms become distributed applications integrated by users and useful for  Workflow  Data entry and viewing  Planning and problem-solving  … ~1 day to integrate using links  Users and developers adapt jointly  Users can adapt the data model immediately  Users can annotate requirements for AI and tailor some rules  Developers understand domain and requirements from forms and annotations  Developers may clean up data model  Process continues indefinitely because it’s cost-effective

21 21 Building and Exploiting a Structured Data Model Based on Forms Active Forms A way to build it Immediate payoff Reduced resources Bottom-up approach Active Forms Technology A way to introduce and integrate it Workflows Briefings Planning Users create forms to capture structured parts of these and get smart help for their effort A way to update and improve it Edit state change once Never duplicate effort Improve as needed Requirements for it Manual search for information Decision support Bandwidth limitations Multi-level security Neat things you can do with it Regular layout helps you find key info Heuristic rules define defaults Case-based reasoning based on experience General procedures and web agents Constraint checking Machine learning from interaction

22 22 The World-Wide Web Analogy Evidence that users can help technology scale

23 23 Technical Problem We have a great information system (the web), but we have no large-scale system for smart automation… and none is coming DARPA’s $80M investment in agents and the Semantic Web are useful but not sufficient because they are:  complex  top-down  slow to payoff Source: Hobbes: HTTP HTML Browsers Number of Smart Agent Systems 1999: : 30 Number of DAML Ontologies 1999: : 112

24 24 We want this kind of growth curve for smart automation systems We can achieve it with tools that enable anyone to create smart templates that can be stitched together to create complete systems simple bottom-up immediate payoff Technology Potential And we’ll get composable micro-ontologies and structured data as a side-effect

25 25 5 th Generation Applications

26 26 Bottom-up, inside-out, do-it-yourself  Bottom-up: don’t try to define a large model  Depend on composition to get a larger model  Inside-out: expose the small model for use by others  Do-it-yourself: avoid the cost and scalability limitations of knowledge engineering  Results:  Faster application development  Cheaper  More scalable

27 27 Smart automation that’s “Web-simple” HTTP HTML Browsers Relational Database XML Forms Rules and Case Depends You can create a complete web site in a day… You should be able to create a smart workflow in a day too! Users create these forms by specifying the variables, the appropriate widgets, and perhaps by enumerating values

28 28 Structure On-the-Fly Bottom-up Micro-Ontology Terms Relationships between them # If your destination is less than 300 miles away, # then you probably should just drive your own car rather than fly # If can't figure out how far away your destination is, don't suggest anything set rule(Mode) { if {[destinationMilesLessThan 300]} { set el(Mode.suggestedvalue) DrivePOV } elseif {[destinationDistanceUnknown]} { reset el(Mode.suggestedvalue) } else { set el(Mode.suggestedvalue) Fly } Every template defines a micro ontology that comes for free layered context namespace But we do create “structure on-the-fly” This semantic information might need cleanup

29 29 Structure On-the-Fly Bottom-up Micro-Ontology Terms Relationships between them # If your destination is less than 300 miles away, # then you probably should just drive your own car rather than fly # If can't figure out how far away your destination is, don't suggest anything set rule(Mode) { if {[destinationMilesLessThan 300]} { set el(Mode.suggestedvalue) DrivePOV } elseif {[destinationDistanceUnknown]} { reset el(Mode.suggestedvalue) } else { set el(Mode.suggestedvalue) Fly } Every template defines a micro ontology that comes for free layered context namespace But we do create “structure on-the-fly” This semantic information might need cleanup

30 30 Application categories Planning Interfaces to external problem-solvers Information editing and viewing

31 31 Application categories A structured form of

32 32 5 th Generation Applications Create and Sustain Structured Data Old riddle: “Which came first, the chicken or the egg?” Answer for smart software: They must be co-developed  5 th Generation Applications (e.g. distributed forms):  Create structured information by capturing decisions, accepting inputs, and AI  Depend on structured information from other sources, including other 5 th Generation Apps  Add value to workflows and problem-solving  Are easily extended to cover new problems or to cover existing problems better  Viola! Mutual support  All of these factors support the sustainability and extensibility of structured data and the family of smart algorithms needed for intelligent assistance and improved C2 capability


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