Toward Practical Knowledge Based Tools for Battle Planning and Scheduling

Alexander Kott Larry Ground Ray Budd BBN Technologies Lakshmi Rebbapragada Army CECOM John Langston Austin Information Systems

Views expressed in this paper are those of the authors and do not necessarily reflect those of the U. S. Army or any agency of the U.S. government.

Outline Problem The CADET System Key Engineering Decisions Challenges Ahead Other Domains

Problem

• Building an operation (e.g., battle) plan for a large, complex, military force, e.g., US Army Brigade or Division • Performed by a planning cell • Trucks, tents, maps, acetate sheets • Begins w/ Cmdr sketch and statement • Follows Military Decision Making Process (MDMP) • Most time-consuming steps: COA development, analysis • Challenge: tasking, allocation, synchronization • Challenge: estimations of time-space, resources, consumption, attrition

Example of a Battle Plan Schedule – Synch. Matrix

Timeline (H hours) Classes of tasks Tasks w/ time, place, resources

Example of an Order

Resources (units) Tasks w/ time, place, resources Terrain features, units, reference lines

The Function of CADET

Key Inputs:

 COA Statement (object represented, 5-10 main activities)  Friendly assets, strength, location  Enemy COA, assets, strengths, location  Environment (terrain, etc.)

CADET

Application domains: US Army Div, Bde operations, intel ops… Intended users: Bde planning staff officers Role: COA analysis/ wargaming of the US Army MDMP Tool sponsors: Army CECOM, BCBLs, DARPA Key Outputs:

 Detailed Plan    200-500 activities all BOS’s timing, synchronization  assets allocated  Estimates  attrition  consumption

How CADET is Used

Using COA Entry tool, officer enters digitized operational concept: sketch and statement The staff reviews and modifies CADET’s products

COA Entry

COA Entry sends digitized COA sketch and statement to CADET OPORD, OPLAN, FRAGOs are generated and issued

CADET

CADET generates detailed, synchronized plan and estimates

1-42 DESTROY3 1-44 Follow MRP_C 1-42 1-44 Tactical March 1-44 Cross PL PL SWORD 1-42 Tactical March 1-42 Uncoil 1-44 Tactical March 1-44 Cross PL PL SWORD 1-42 Tactical March 1-42 Cross PL PL SWORD 1-44 Tactical March 1-44 Cross PL PL RED 1-42 Tactical March 1-42 Cross PL PL RED 1-44 Tactical March 1-44 Cross PL PL CHICAGO DIVARTY Prep Fire for Attk Observe And Report with UAV Position UAV Locate MRP_B with vehicle 2 from UAV Div MI Bn Assess Enemy Force MRP_A Arty Btry B Prep Fire for Attk UAV Position Sensor Team B Provide coverage for area Div MI Bn Assess Enemy Force MRP_B Launch Sensor Sensor Team A Re-position to provide continuous coverage Arty Btry B Provide coverage for area Arty Btry A Re-position to provide continuous DIVARTY Prep Fire for Attk Eng Co Move along route with Advanced Guard Eng Co Update Movement plan Eng Co Reconnaissance for Movement to contact ADA Team B Provide coverage for area UAV Move along route with Advanced Guard

POL TOW BGM-71F 120MM Mortar 25MM shell 87% 96% 96% 96% 1-42 Consumption/Attrition Calculations Weapons System 92% Personnel POL 94% 99% 120MM HEAT-MP-T M830 SABOT: 120MM APFSDS T M829A1 120MM Mortar 100% 100% 100% 1-43 Consumption/Attrition Calculations Weapons System 87% Personnel 90% POL 64% 120MM HEAT-MP-T M830 SABOT: 120MM APFSDS T M829A1 120MM Mortar 59% 59% 59% RES Consumption/Attrition Calculations Weapons System 100% Personnel 100% POL 80% 86% 96% 96% 96% 92% 94% 98% 100% 100% 100% 85% 89% 61% 55% 55% 55% 100% 100% 78% 86% 96% 96% 96% 92% 94% 98% 100% 100% 100% 84% 88% 58% 51% 51% 51% 100% 100% 77% 85% 96% 96% 96% 92% 94% 98% 100% 100% 100% 83% 87% 55% 47% 47% 47% 100% 100% 76% 82% 92% 92% 92% 92% 94% 97% 100% 100% 100% 81% 86% 52% 43% 43% 43% 100% 100% 75% 79% 87% 87% 87% 88% 90% 94% 96% 96% 96% 80% 85% 49% 39% 39% 39% 100% 100% 73% 76% 83% 83% 83% 83% 87% 91% 92% 92% 92% 79% 84% 46% 35% 35% 35% 73% 79% 79% 79% 100% 100% 72% 100% 100% 71% 77% 83% 42% 31% 31% 31% 78% 83% 88% 87% 87% 87%

A Key Engineering Decision: Interleaving

Challenge: strong coupling of multiple problem aspects planning affects scheduling scheduling impacts suitability of activities both impact routing routing impacts the required activities attrition and consumption impact activities, timing Significant: enemy acts as the key factor in this strong coupling

Interleaving: “plan a little, schedule a little…”

interleaved increments of planning, routing, time estimating, scheduling, estimates of attrition / consumption small increments rely on assumptions based on prior decisions size of an increment: larger is less informed, smaller – less optimal experimental compromise: 10-20 activities, also convenient for user’s review planning scheduling logistics attrition movements

A Key Engineering Decision: Action-Reaction-Counteraction

Challenge: enemy has a critical vote in every decision; movements and action of enemy units impact all aspects of the problem Our approach: Decided against game-theoretic approaches Adopted a known manual heuristic: A-R-C For each Action (Friendly), estimate the likely Reaction (Enemy), then produce Counteraction (Friendly) Each Reaction or Counteraction may be complex Not the same as a 2-ply game!

Further “plies” not valuable CADET extends A-R-C by parallel planning for both friendly and enemy forces

A Key Engineering Decision: UI Independence

On one hand:   A decision-support system is 80% about UI You need UI for a good demo and to get $$ On the other hand:  Too many people building similar-looking UIs    Good UI leaves no money for good AI A deployment environment would have its own UI Can conventional UI concepts apply to this problem (time, stress, representation)? Need new concepts

UI Independence

Bare-bones UI for developers and demos Rigorous avoidance of UI assumptions XML-based, flexible engine for inexpensive integration w/ UI Integration w/ a number of systems with different UIs

ASAS-L, BCBL-L BPV, Army CECOM COA Creator

Challenges Ahead: Field Maintenance of Knowledge

Extreme demands on KB maintenance:  In the field  By non-programmers A partial answer:  Simple templates   No provisions for programming A 70% solution?

A route should be selected so that the unit moves through the destination area Maneuver unit advance logic should be used to model the unit movement An objective area is required The unit candidate criteria, and BOS are specified Given that the seize is supported, the domain expert assesses that the unit performing this task will receive only 90% of the attrition of a normal engagement

Challenges Ahead: Distributed Collaboration Must provide for:

      Multiple users – integrated plans Partial plans by coalition members Capture, resolve inconsistencies Asynchronous Geographically dispersed No, it’s not about a better electronic white board

Challenges Ahead: Dynamic Continuous Replanning

Once execution starts, the battle plan immediately deviates from reality Ideally, commanders and staff would like to perform rapid replanning within execution Performance of algorithms is not critical Manual input of commander intent, concept is critical Understanding of execution stability is critical

Other Domains: Robot Human Teams in Special Ops

I S B AA Whi skey drop I S B

TF Fa lco n TF Ha wk TF Fal con

m 1

Sna ke

drop Team drop Tea m 5 Sie rra

Ha wk

AA Whis key AA Sier ra

16 units/teams

•

Robots

•

TacAir

•

Helos

•

Ground elements

•

Indirect fire sets

Other Domains: Disaster Response

At the City-County Emergency Operations Center, the staff monitor and visualize the situation: multiple coordinated terrorist attacks in the City The system produces recommendations as a detailed schedule of tasks: resources and supplies; temporal dependencies; need for resupply and rest; safety of the respondents; balances immediate response vs. downstream needs the system considers routes and movements of the units “juggled” from site to site, accounting to availability of roads and bridges, flows of refugees, etc.

Conclusions

An instructive example of a (common) real-world, non-decomposable problem Interleaving can be an effective practical approach to such problems A-R-C heuristic is useful for adversarial problems and may have strong theoretical justification UI is not always a good investment Key remaining challenges: distributed collaboration and dynamic, stable replanning Intriguing possibilities in other problem domains

BACKUP SLIDES

Interleaving and Backtracking

Minimal or no backtracking: Infeasibilities are best resolved by the user, and only after he sees “the whole” Often accepted and even expected Clean resolution often calls for change in sketch and-statement Look-ahead and non-sequential expansion: Unlike simulation or wargaming Heuristics for focusing on most critical activities first Not necessarily sequential to those already planning

Architecture for Interleaving

XML Engine -translates in/out -Xerces, Xalan exists To be Collab. Analyzer, Merger In-Execution Replan Analyzer End-User Task Modeling Tool Temporal Constraint Mgr Expander/Scheduler -interleaved process Synch Matrix Interface Attrition Calculator -Fast -Calibrated -Incl. Timing… Task Models -expansion methods - timing, resources Route Calculator -fast -multi-var optimiz.