Failures in C2 Technology Why command and control has stagnated Doug Dyer April 03

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

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 5th Generation Applications • Bottom-up, inside-out, do-it-yourself • Applications: workflow, structured email, interfaces to problem solvers, data editing • Applications as creators and reasons to sustain structured data

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

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

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

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

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

Structured information is the foundation for new C2 capability • 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 Dialog & machine learning Generative Planning Case-based reasoning Procedures Constraint checking Web Services Simulations Rules Tailored Feeds Sentinels Assessment of coverage Rapid situation understanding Standard Presentation Formats Structured Data Model

Joint Vision 2010 Plan and Situation Data Models Again, JV2010 had the right idea… But JV2010 never successfully implemented the models… No other C2 effort has created an enduring data model 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

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

Smart algorithms are the keys to decision speed and qualityAssumed: future operations will be more complex • Some might disagree based on past efforts, butmachine-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 Dialog & machine learning Generative Planning Case-based reasoning Procedures Constraint checking Web Services Simulations Rules Tailored Feeds Sentinels Assessment of coverage Rapid situation understanding Standard Presentation Formats Structured Data Model

A Joint Vision 2010 Functions and Algorithms Again, JV2010 had the right idea… But even JV2010 demos couldn’t meet the vision No other C2 effort has fielded smart, large-scale automation 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

Defining Structured Information

“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

Structured Information for Command and ControlDefining an “Information Element” An information element is an atomic unit of structured information It includes: Context Variable Value Meta-Data Example: 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

Structured InformationDefining an “Information Element” An information element is an atomic unit of structured information It includes: Context Variable Value Meta-Data 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 Example: 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

A structured data model represents plans and situationsSDM: a set of information elements Structured Data Model Context Variable Value Meta-Data C1 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

How to build a structured data model

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

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 Two ways to build a structured data model

Building and Exploiting a Structured Data Model Based on Forms • 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 • A way to update and improve it • Edit state change once • Never duplicate effort • Improve as needed 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 • Email • Planning • Users create forms to capture structured parts of these and get smart help for their effort • Requirements for it • Manual search for information • Decision support • Bandwidth limitations • Multi-level security

The World-Wide Web AnalogyEvidence that users can help technology scale

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 HTTP HTML Browsers Source: Hobbes: http://www.zakon.org/robert/internet/timeline/ • Number of Smart Agent Systems • 1999: 50 2003: 30 • Number of DAML Ontologies • 1999: 112 2003: 112

Technology Potential • 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 And we’ll get composable micro-ontologies and structured data as a side-effect

5th Generation Applications

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

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

Structure On-the-FlyBottom-up Micro-Ontology • Terms • Relationships between them • Every template defines a micro ontology that comes for free • layered context • namespace This semantic information might need cleanup # 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 } } But we do create “structure on-the-fly”

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

Application categories • A structured form of email

5th Generation Applications Create and SustainStructured Data • Old riddle: “Which came first, the chicken or the egg?” • Answer for smart software: They must be co-developed • 5th 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 5th 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

Failures in C2 Technology Why command and control has stagnated Doug Dyer April 03