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Models, Gaming, and Simulation - Session 9. Command, Control, Communications, Computers, and Intelligence (C 4 I). PURPOSE: Consider how C 4 I (especially C 2 ) can be modeled in a combat simulation. Topics. Definitions Command and Control The Process Modeling C2

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models gaming and simulation session 9

Models, Gaming, and Simulation - Session 9

Command, Control, Communications, Computers, and Intelligence (C4I)

topics

PURPOSE: Consider how C4 I (especially C2) can be modeled in a combat simulation.

Topics
  • Definitions
  • Command and Control
    • The Process
    • Modeling C2
  • Communications and Computers
    • The Process
    • Modeling Communications and Computers
  • Intelligence
    • The Process
    • Modeling Intelligence
definitions one set many variants exist
Definitions(one set; many variants exist)
  • Command: Decision-making; the formal exercise of authority and direction over an organization in the accomplishment of a mission.
  • Control: Implementation of decisions
  • Command and Control (C2): the process of
    • Acquiring information
    • Assessing the situation
    • Deciding subsequent intentions
    • Directing subordinates and Reporting to superiors
  • Communications: The electronic means of directing and reporting
  • Computers: The electronic means of automating the C3I process
  • Intelligence: The process of acquiring information and assessing the enemy and environmental situation.

NOTE: This is a behavioral approach to C4 I definition; other, more technical approaches view C4 I as the structure and equipment by which the commander exercises command and control.

the process being modeled c2
The Process Being Modeled - C2

The Conceptual C2 Process Model (one version):

Acquire

Environment

Assess

Decide

Own forces

Generate

Select

Plan

Direct

the process being modeled c25
The Process Being Modeled - C2
  • A unifying theme: coping with uncertainty
    • "The history of command in war consists essentially of an endless quest for certainty about the state and intentions of enemy forces, about the ... environment, and about the state, activities, and intentions of one's own forces" - M. Van Creveld, Command in War
    • "War is the province of uncertainty: three-fourths of those things upon which action in war must be calculated, are hidden more or less in the clouds of great uncertainty." - C. von Clauswitz, On War
    • "Intelligence is still a very uncertain, fragile, and complex commodity." - William J. Casey, former Director of Central Intelligence
the process being modeled c26
The Process Being Modeled - C2
  • Planning
  • Decision-making
  • Execution of a plan
the process being modeled planning
The Process Being Modeled - Planning

U.S. Army's Battle Planning Process (”Military Decision-Making Process")*

*US Army Field Manual 101-5, Staff Org. & Opns.

MISSION ANALYSIS

Facts

Assumptions

Mission and Intent

COURSE OF ACTION ANALYSIS AND COMPARISON

Wargame each COA

Compare COAs

Recommend a COA

COURSE OF ACTION DEVELOPMENT

Relative force ratios

Array initial forces

Develop Scheme of Mvr

Determine C2 means and maneuver control measures

DECISION, PLAN PREPARATION, AND EXECUTION

the process being modeled planning8
The Process Being Modeled - Planning
  • DEFINITION:
    • Planning is deciding on a course of action before acting.
  • PURPOSE: Develop a sequence of actions to achieve a goal.
  • DEFINITION:
    • A Planis a representation of a course of action.
      • More correctly, a plan is a partially ordered set of goals, each of which can be replaced by a subplan or a primitive action.
      • Alternately, a plan is a hierarchical process which can control the order in which a sequence of operations is to be performed.
modeling planning various approaches
Modeling Planning - Various Approaches
  • Scripting (Manual planning, scripted execution)
    • User times and plots all movement.
    • C2 is really only in the mind of the user.
  • Plan-Based (Manual planning, automatic execution)
    • User prepares orders for each headquarters and resolution unit (preferably using a "plan editor").
    • Orders are written in terms of unit objectives (e.g., "move to objective X")
    • Units automatically find routes.
    • Major decisions are prepared for ahead of time by creating branches and sequels to a plan, alternate plans, and "frag orders".
  • Plan-Based, Limited Automated Planning
    • Resolution units have default ways of accomplishing their missions.
    • Headquarters’ orders prepared by user, perhaps with standard mission templates.
  • Automated Planning
    • All planning is done by the computer (not well-implemented in any mainstream combat model)
automated planning four approaches
Automated Planning - Four Approaches*

*Artificial Intelligence Handbook, Vol III

  • Non-hierarchical
    • Plan is represented as a sequence of actions.
    • Approach may be goal-subgoal, or means-end analysis.
    • Characterized by bottom-up development; no differentiation between essential and non-essential details.
  • Hierarchical
    • Plan is represented as abstract plan and more specific sub-plans.
    • Approach is to sketch a vague, but complete plan, then refine vague parts into more detailed sub-plans.
  • Skeleton Plans (“Case-Based”)
    • Search a database of plan outlines, or complete plans, indexed by the situation and goal. Fill out the outline, or modify the complete plan, as needed.
  • Opportunistic (“Blackboard”)
    • Plan is filled in by various specialist agents operating independently, as the agents see a piece they can solve.
modeling decision making
Modeling Decision-Making
  • If plan is scripted, no decision-making is needed, but the model should not claim to model C2.
  • Two levels of decision-making
    • Default, or Standing Operating Procedures - usually for resolution units
    • Planned decision-making - a key decision based on the situation causes a branch in the plan - usually found in headquarters units
  • Three common implementations of decision-making
    • Human-in-the-Loop decisions
    • Decision-tables or decision-trees
      • When a few standard decisions can cover all possible situations
      • Used in first-generation C2 models
    • Rule-based reasoning
      • When a wider range of decisions are to be represented
      • When user input of decision-making rules is desired
      • Used in second-generation C2 models
modeling decision making12

Enemy perceived strength:

Y > 80%

50%< Y < 70%

Y < 50%

Own force

strength:

Attack

Attack

Attack

X > 80%

Defend

Defend

Attack

70% < X < 80%

50% < X < 70%

Delay

Defend

Defend

20% < X < 50%

Withdraw

Delay

Defend

X < 20%

Withdraw

Delay

Surrender

Modeling Decision-Making
  • Example decision table:
  • Example rule-based reasoning:

IF ((main enemy attack has been detected) and

(counter-attack force is ready))

THEN (launch counter-attack)

modeling plan execution example
Modeling Plan Execution - Example
  • Taken from Eagle
    • Corps-level
    • Battalion resolution
    • Rule-based decision-making
    • Plan-based with limited automated planning
  • General Approach:
    • Resolution unit’s behavior is goal-driven:
      • Seize (move to) its objective
      • Adopt best posture (“operational activity”) for situation.
    • HQ units move their plan forward from phase to phase.
      • When starting a phase, HQs send new tasks and objectives to subordinates
      • HQs change to a new phase based on rules which model decision-making and execution monitoring.
modeling plan execution example14
Modeling Plan Execution - Example
  • Given a typical (though simplistic) order from higher, how does an actual brigade staff plan to execute the order?
  • Example: 1st Brigade, attack along Axis Hammer at 0600 hrs to sieze Objective Rock.
  • Operations Overlay:

ROCK

HAMMER

modeling plan execution example15

ALPHA

WHITE

ROCK

GAMMA

BETA

BLUE

RED

Modeling Plan Execution - Example
  • The Brigade constructs its plan, decomposing its mission into phases and giving tasks and intermediate objectives to its subordinates.

PL INDIGO

PL INDIGO

modeling plan execution example16
Modeling Plan Execution - Example
  • What kinds of information are needed to represent this operations plan (OPLAN)?
    • Military units with their own weapons, knowledge, and capabilities.
      • Resolution units - fight
      • Headquarters units - monitor and control plans
    • Control Measures
      • Objectives
      • Phase Lines
      • Axes of advance
    • Unit postures, e.g., percent moving/stationary, orientation, etc.
    • Unit roles, e.g., “left-lead battalion”, “follow-and-support battalion”.
    • Phases, i.e., tasks for each subordinate and rules to begin the phase
    • Tasks, i.e., missions, for example: “attack”, “defend”, “delay”.
  • Each of the above is a class of software objects in Eagle.
  • The OPLAN is a key data structure which ties them together.
modeling plan execution example17
Modeling Plan Execution - Example
  • Let’s watch how a brigade controls one of its battalions in executing part of a plan:
    • DIVISION DIRECTS BRIGADE X : “CHANGE TO PHASE 2 “
      • Brigade X interprets this as the Division’s Phase 2.
      • Brigade X looks in Phase 2 in the Division OPLAN and finds its first task: ATTACK to OBJ ROCK
      • Brigade X looks in its own OPLAN at this task, which corresponds to its own Phase 5.
    • BRIGADE DIRECTS EACH SUBORDINATE: “CHANGE TO PHASE 5”
    • BATTALION Y RECEIVES THIS DIRECTIVE
      • Battalion Y looks up its first task in Brigade’s Phase 5 and finds: ATTACK to OBJ GAMMA along AXIS BLUE.
modeling plan execution example18
Modeling Plan Execution - Example
  • Now how does the battalion respond?
    • BATTALION establishes its state:
      • TASK: ATTACK (copied from Brigade order)
      • OBJECTIVE: GAMMA (copied from Brigade order)
      • MANEUVER GUIDANCE: AXIS BLUE (copied from Brigade order)
      • RELATION WITH OBJECTIVE: NOT “AT” (comparing computed distance with own SOP about relationship definitions, e.g., AT=1km)
      • MOVE DESIRE: MOVE (concluded by assessing “move rules” in battalion’s SOP for ATTACK tasks)
      • INTENTION: BEGIN MOVING TO OBJECTIVE (concluded by assessing “intent rules” in battalion’s SOP for ATTACK tasks)
      • OPERATIONAL ACTIVITY: TRAVELLING-OVERWATCH( concluded by assessing “OA rules” in battalion’s SOP for ATTACK tasks)
      • ROUTE: ROUTE-X3928 (computed using A* from current location to OBJ GAMMA constrained to stay within AXIS BLUE)
modeling plan execution example19
Modeling Plan Execution - Example
  • What does the battalion do next?
    • During the simulation’s next MOVE cycle, it checks its MOVE DESIRE, ROUTE, and OA attributes. Finding appropriate values, it computes a speed and moves along ROUTE-X3928.
    • During the simulation’s next LOOK cycle, the battalion computes the state of its local situation map by remembering previous sitmaps, and applying the appropriate search algorithm to detect nearby units.
    • During the simulation’s next DECIDE cycle, it reassesses its state variables.
      • RELATION WITH OBJECTIVE: still NOT “AT”
      • MOVE DESIRE: still MOVE
      • INTENTION: CONTINUE MOVING TO OBJECTIVE (concluded by assessing “intent rules”)
      • OPERATIONAL ACTIVITY: STILL TRAVELLING-OVERWATCH
modeling plan execution example20
Modeling Plan Execution - Example
  • What if the plan doesn’t go exactly as planned?
    • During the simulation’s next SHOOT cycle, assume an enemy battalion fires at it, causing casualties in Battalion Y.
    • During the simulation’s next LOOK cycle, Battalion Y gathers information about the enemy as allowed by the search algorithm.
    • During the simulation’s next DECIDE cycle, it reassesses its state variables, deciding to divert to attack the local enemy.
      • INCOMING-INTENSITY: HEAVY-DIRECT-FIRE (computed from losses during the past 30 minutes)
      • INTENTION: ATTACK LOCAL ENEMY
      • OBJECTIVE: changes to LOCAL-ATK-OBJ-Y2567
      • RELATION WITH OBJECTIVE: NOT “AT”
      • ROUTE: new route found from current location to LOCAL-ATK-OBJ
      • OPERATIONAL ACTIVITY: changes to BOUNDING-OVERWATCH
modeling plan execution example21
Modeling Plan Execution - Example
  • Now Battalion Y will attack the local enemy
    • When Battalion Y arrives at its temporary objective, and is no longer under heavy direct fire, it will retrieve its original objective GAMMA.
    • During the simulation’s next DECIDE cycle, it reassesses its state variables, deciding return to its original task to attack to OBJ GAMMA.
      • INCOMING-INTENSITY: NONE
      • OBJECTIVE: changes back to GAMMA
      • ROUTE: new route found from current location to GAMMA
      • INTENTION: BEGIN MOVEMENT TO OBJECTIVE
  • When Battalion Y reports “AT GAMMA”, the Brigade decides to launch Battalion Z along AXIS RED, i.e., Brigade has a “Task-change” rule:
    • IF(BnY AT GAMMA) THEN (CH-TASK for BnZ to TASK 2)
modeling plan execution summary
Modeling Plan Execution - Summary
  • So the fundamental principle driving resolution unit behavior is the drive to be AT its current OBJECTIVE.
  • And the principle driving headquarters unit behavior is the drive to ADVANCE ITS PLAN by testing and following its RULES.
the process being modeled computers
The Process Being Modeled - Computers
  • "Digitization of the Battlefield"
    • The tools:
      • Global Positioning System - each vehicle knows its exact location
      • Intervehicular Information System - vehicles share information
      • Army Tactical Command and Control System - headquarters share information in five dimensions
        • maneuver
        • indirect fire
        • air defense
        • intelligence
        • logistics
    • The vision: commanders reduce uncertainty about the friendly and enemy situation because of automated information sharing.
eagle s comms and computers
Eagle’s Comms and Computers
  • Communications
    • Implicit networks: one link between each HQ
    • Various message types
    • Messages are delayed for:
      • processing and decision-making
      • transmission
  • Computers
    • Implicitly modeled by speeding up orders-related messages, intelligence fusion, and /or delivery of fires.
the process being modeled intelligence
The Process Being Modeled - Intelligence
  • Components
    • Ground Reconnaissance
    • Sensor Acquisition
    • Reporting
    • Intelligence Fusion
    • Analysis
  • Products
    • Decision-making information
    • Targeting information

Now let’s look at a possible way to model the intelligence process

eagle s intelligence
Eagle’s Intelligence
  • Collection Plan prepared by user for each phase of each HQ’s plan
    • Named Areas of Interest / Targeted Areas of Interest (NAI / TAI) specify locations of interest (where to look)
    • Priority Information Requirements (PIR) specify questions about the enemy, answers to which are critical to the commander’s decision-making process (what to look for)
    • Collection assets are allocated to each NAI / TAI by phase (who will look)

(continued...)

NAI / TAI

eagle s intelligence28
Eagle’s Intelligence
  • Collectors
    • Maneuver unit SITREPS are passed up the chain of command and help set each HQ’s perception of the battle.
    • Sensors report to tasking HQ per collection plan:
      • Sensors observe in the specified NAI / TAI
      • Sensors acquire targets according to the signature the targets are emitting:
        • COMINT / ELINT (based on communications density)
        • PHOTO (based on numbers of vehicles present)
        • MTI (based on numbers of moving vehicles present)
        • CM / CB (based on fire missions recently fired)
    • Sensors which are “down-linked” on a fire support “quick-fire” net pass targeting data as soon as acquisitions are made.
eagle s intelligence29

Infer

Infer

“Enemy is conducting his main attack in TAI 43”

Eagle’s Intelligence
  • Intelligence Analysis
    • “First-order fusion” - merges vehicle sensings in an NAI into units identified by type, or size, or role, etc.
    • “Second-order fusion” - matches identified units with Priority Information Requirements.
    • Targeting information is passed to the fire support staff when target units are confirmed. (20- to 30-minute process)
modeling intelligence reconnaissance
Modeling Intelligence - Reconnaissance
  • Each unit detects other units using its organic sensors (e.g., tank sights, battalion radars)
  • For each target unit within some max range, the sensing unit determines:
    • how many sensors of sensing unit are eligible to acquire.
    • how many systems in target unit are eligible to be detected.
    • how many target systems are acquired of each type
    • given the total number of systems detected, what size and type target unit does the sensing unit conclude it has found ("first-order fusion"). First-order fusion represents the sensing unit's recognition that there might be multiple detections of a single vehicle.
  • Sensing unit sends a message to its higher headquarters reporting each target unit it thinks it has found.
eagle s reconnaissance
Eagle’s Reconnaissance
  • Battalion’s detect other units implicitly within their search pattern.
  • Scouts, surveillance radars, SOF teams, etc. can be played explicitly as sensors.
  • Scout Platoons, Divisional Cavalry Troops and Squadrons, and Cavalry Regiments can be played explicitly as maneuver units.
modeling intelligence reconnaissance example
Modeling Intelligence - Reconnaissance Example
  • How many sensors are eligible to acquire?
    • Sensing unit has 50 tanks, and is in a posture that has 50% of its vehicles positioned forward and 80% stationary (i.e., 20 tanks forward stationary, 5 tanks forward moving)
  • How many target systems are eligible to be detected?
    • Target unit directly to front of sensing unit has 100 IFVs (infantry fighting vehicles), and is in a posture that has 30% of its vehicles forward and 30% stationary (i.e., 9 IFVs forward stationary and 21 IFVs forward moving)
  • How many target systems are acquired?
    • For each of the 25 detecting tanks, apply a target detection algorithm to determine which targets are detected. Assume 10 IFVs are detected.
  • What size and type target unit does the sensing unit conclude it has found?
    • If the 25 sensing tanks detected 10 IFVs, it might conclude it had found a mech infantry company, or maybe a mech infantry battalion.
modeling intelligence sensor acquisition
Modeling Intelligence - Sensor Acquisition
  • Each sensor unit detects other units using its organic sensors (e.g., JSTARS, unmanned drones, counter-mortar/counter-battery radars)
  • Sensor unit has distributed its sensors so that each one is looking into a specified area ("Named Area of Interest", or NAI). For each target unit in each NAI, determine:
    • how many systems in target unit are eligible to be detected.
    • how many target systems are acquired.
    • given the total number of systems detected, what size and type target unit does the sensing unit conclude it has found ("first-order fusion").
  • Sensing unit sends a message to its higher headquarters reporting each target unit it has found.
modeling intelligence sensor acquisition example
Modeling Intelligence - Sensor Acquisition Example
  • How many target systems in an NAI are eligible to be detected?
    • Target unit has 100 IFVs. It is in a given NAI in prepared defensive positions, with 50% of its systems under overhead cover or concealment. Therefore, 50 IFVs are eligible for detection.
  • How many target systems are acquired?
    • For each sensor looking in the NAI, apply a target detection algorithm to determine whether and how many targets are detected, considering sensor posture and target posture. Assume 2 sensors detected 10 IFVs each.
  • What size and type target unit does the sensing unit conclude it has found?
    • Fuze detections to estimate 16 IFVs total detected in NAI. Conclude it has found a mechanized infantry battalion.
modeling intelligence second order fusion and analysis
Modeling Intelligence - Second-order Fusion and Analysis

Each unit keeps a "Situation Map", i.e., a list of detected units and their estimated locations as provided by its own sensors or reports from subordinates,its higher headquarters or a supporting intelligence unit.

The unit estimates the total number of resolution units it has found, fuzing multiple detections (sometimes called "second-order fusion")

It estimates the organizational structure of the resolution units, perhaps considering communications nodes as headquarters, so that it can provide an intelligence estimate of the form: "We are opposed by a mechanized infantry division with one tank-heavy brigade in reserve and two mech-heavy brigades attacking" (sometimes called "analysis").

NOTE: This step is a very difficult problem, and not too many (if any) models represent it well. The algorithm could use a doctrinal or situational template of the enemy and try to fill roles in the template with detected units. The template which is best matched by the detected units is chosen as the best estimate of the enemy situation.

The unit reports its intelligence estimate to its higher headquarters, its subordinate headquarters, and (if it is an intelligence unit), its supported headquarters.

modeling intelligence using the intelligence product
Modeling Intelligence - Using the Intelligence Product
  • Each headquarters has a list of "Priority Information Requirements" (PIR), i.e., questions it needs answered about the enemy situation. These could take the form of the "IF" clauses of rules.
  • Whenever it receives intelligence reports, it adds to a knowledge base of facts that it will consider to be true.
  • During the "Decide" phase of command and control it will check to see if any PIRs are satisfied. If so, the "THEN" clause of the rule tells the headquarters what to do about it. Typically, the "THEN" clause might have the headquarters
    • advance the plan to a new phase
    • change plans - cause the whole command to execute a different plan
    • issue a "Frag Order" to a subordinate - cause the subordinate to do something different than specified in the basic plan