1. Command and Control Modeling for Synthetic Battlespaces: Flexible Group Behavior Randall W. Hill, Jr. Jonathan Gratch USC Information Sciences Institute ASTT Interim Progress Review May 24, 1999

2. Agenda zSynthetic Forces Problem zProgram Hypotheses zTechnologies and R&D zSignificant Results & Expected Results zTechnology Transition Products & Efforts zProblem Areas zProgrammatic Issues

3. Synthetic Forces Problem

4. Problem zNeed cost-effective C2 modeling yReplace / augment human controllers with automated C2 yRepresent a wide range of organizations and situations zNeed realistic C2 behavior yC2 models must make believable decisions yThe outcomes of C2 operations need to be credible

5. Project Goals zDevelop autonomous command forces yAct autonomously for days at a time xReduce load on human operators yBehave in human-like manner xProduce realistic training environment yPerform C 3 I functions xReduce the number of human operators xCreate realistic organizational interactions

6. Program Hypotheses

7. Hypotheses zFlexible behavior requires the ability to handle situation interrupts zFlexible group behavior requires: yUnderstanding behavior of groups of entities yPlanning a mission for groups against groups yExecuting a mission in a coordinated manner

8. Hypotheses zFlexible group behavior interleaves the processes of situation assessment, planning, execution, and plan repair zCoordinated group behavior requires a theory of multi-agent interaction

9. Technologies and R&D

10. Technologies zContinuous Planning yDepends on understanding evolving situations yImplements planning as a dynamic process yAchieve goals despite unplanned events zCollaborative Planning yCoordinate group behavior yRequires understanding behavior of other groups yReason about organizational constraints

11. Technologies zSituation Awareness yCurrent situation xNeed a consolidated picture xRequires situation assessment at multiple echelons yFuture situation xIntegrate planning with future sensing requirements xFormulate Priority Intelligence Requirements (PIR)

12. Mission Capabilities zArmy Aviation Deep Attack yBattalion command agent yCompany command agents yCSS command agent yAH64 Apache Rotary Wing Aircraft ySuppression of Enemy Air Defense (SEAD) by indirect fire (partially implemented) yIntelligence assets (partially implemented)

13. BP FARP CSS HA FLOT SEAD SLAR MLRS Battalion Deep Attack

14. …. Operations Order (plan) C2 Architecture Battalion Commander Company A Commander Company X Commander Company A Pilot Helicopter Pilot Helicopter Pilot Helicopter ModSAF Company X Pilot Helicopter Pilot Helicopter Pilot Helicopter …. Operations Order (plan) Operations Order (plan) Situation Report (understanding) Situation Report (understanding) Situation Report (understanding) Percepts Actions Percepts Actions

15. Architecture zPlanner yImplements continuous planning capabilities zPlan manager yAugments collaborative planning with organizational reasoning and Military Decision Making Process zTime Manager yManages temporal constraints zDomain Theory yMaintains plan management and tactical knowledge zSituation Assessment yFuses sensors, reports, and expectations yGenerates and updates current world view

16. C2 Entity Architecture Planner (General Purpose Reasoner) Plan Manager Management Plans Tactical Plans Management Theory (domain independent) Tactical Domain Theory World Model Situation Assessment Synthetic Battlespace Situation Reports, Sensing Facts, inferences Expectations OPORDER Other Communications

17. Technologies and R&D: Continuous Planning

18. Continuous Planning zPlan generation ySketch basic structure via decomposition yFill in details with causal-link planning zPlan execution yExplicitly initiate and terminate tasks yInitiate tasks whose preconditions unify with the current world yTerminate tasks whose effects unify with the current world zPlan Repair yRecognize situation interrupt yRepair plan by adding, retracting tasks

19. What are Plans? zHierarchically ordered sequences of tasks zPlans capture assumptions yColumn movement assumes enemy contact unlikely zPlans capture task dependencies yMove_to_Holding_Area results in unit being at the HA, (precondition to moving to the Battle_Position) yOPFOR and Co must be at the Engage_area simultaneously

20. Plan Generation Example Destroyed(Enemy) Attack(A, Enemy) Move(A,BP) Engage(A,Enemy) at(A,BP)at(A,FARP) at(Enemy,EA) at(A,BP)Destroyed(Enemy) at(A,FARP) at(Enemy,EA) World Model... init

21. Company A plan Company B plan CSS plan Move Engage Return Move OPFOR Plan Move Battalion Tactical Plans Co Deep Attack Co Deep Attack FARP Operations

22. Situation Interrupts Happen! destroyed(Enemy) Attack(A, Enemy) Move(A,BP) Engage(A,Enemy) at(A,BP)at(A,FARP)at(A,BP)destroyed(Enemy) at(A,FARP) at(Enemy,EA) Current World active(A) Start of OP ADA Attack active(A)

23. Reacting to Situation Interrupt zSituations evolve unexpectedly yGoals change, actions fail, intelligence incorrect zDetermine whether plan affected yInvalidate assumptions? yViolate dependency constraints? zRepair plan as needed yRetract tasks invalidated by change yAdd new tasks yRe-compute dependencies

24. Technologies and R&D: Collaborative Planning

25. Collaborative Planning zRepresent plans of others yExtend plan network to include others’ plans zDetect interactions among plans ySame as with “normal” plan monitoring zApply planning modulators: yOrganizational roles yWhat others need to know yPhase of the planning yStance of the planner wrt phase and role

26. Plan Interaction Example Move(A,BP) Engage(A,Y) Dead(Y) Move(CSS,HQ) at(CSS,HQ) at(CSS,FAA) at(gas,FAA) at(gas,HQ) at(A,BP) at(A,FAA) at(A,BP) at(gas,FAA) Operation Begins Combat Service Support Plan Attack Helicopter Company Plan resupplied(HQ)

27. Planning Stances zAuthoritative yOrder subordinate to alter his plans zDeferential yChange my plans to de-conflict with superior zHelpful yHelp peer to resolve conflicts in plan zSelf-serving zAdversarial yTry to introduce conflict in other agent’s plan

28. Elaboration: Being Helpful zPlanning issues yPropose doing activities that facilitate others’ plans yAvoid introducing threats into others’ plans zCommunication Issues yCollaboration protocols: propose, accept, counter yRelevance reasoning xWhich of my tasks would others want to know e.g. “Honey, I’m going to the market”

29. Elaboration: Self-serving zPlanning issues yNotice things that others might do for me yIgnore threats I introduce into other’s plans Unless that keeps them from doing things for me zCommunication Issues yDeception xe.g. Someone might not help me if the knew what I was really planning

30. Plan Management zMust model when to use different stances yInvolves organizational issues Where do I fit in the organization yStances may need to change over time During COA Analysis, adopt an adversarial stance towards ones own plans zMust model how stances influence planning yHow do we alter COA generation

31. C2 Entity Architecture Planner (General Purpose Reasoner) Plan Manager Management Plans Tactical Plans Management Theory (domain independent) Tactical Domain Theory World Model Situation Assessment Synthetic Battlespace Situation Reports, Sensing Facts, inferences Expectations OPORDER Other Communications

32. When to Use a Stance zModel the collaborative planning process yIncludes management tasks that modulate the generation of tactical plans xTasks refer to specific tactical plans xSpecify preconditions on changing stance yIncludes knowledge of one’s organizational role zPlanner constructs management plans yUse same mechanisms as tactical planning

33. Management Plan Example zExplicitly model the Military Decision Making Process COA Development Authoritative towards subordinates Deferential towards superiors Adversarial towards OPFOR COA Analysis Authoritative towards OPFOR Adversarial towards self (war gaming) TasksStances

34. Implementing Stances zImplemented as search control on planner yPlan manager Takes executing management tasks Generates search control recommendations zExample: Deferential Stance yWhen giving orders to subordinates Indicate subset of plan is fixed ( defer to this ) Indicate rest of plan is flexible yPlan manager enforces these restrictions

35. Interaction Example Move(A,BP) Move(CSS,HQ) at(CSS,HQ) at(CSS,FAA) at(gas,FAA) at(gas,HQ) at(A,BP) at(A,FAA) at(gas,FAA) Initial State Planner Retract Deferential towards Combat Service Support Plan Make CSS Planner defer to Company A’s Plan Manager

36. C2 Entity Architecture Planner (General Purpose Reasoner) Plan Manager Management Plans Tactical Plans Management Theory (domain independent) Tactical Domain Theory World Model Situation Assessment Synthetic Battlespace Situation Reports, Sensing Facts, inferences Expectations OPORDER Other Communications

37. Technologies and R&D: Situation Awareness

38. Situation Awareness zPlanner needs a consolidated picture of the current situation in the battlespace yDetermines which goals and tasks are achievable yInfluences the choice of strategies and actions yAllows the detection of imminent plan failure yEnables re-planning zSituation assessment produces a current World Model yMonitor plans with respect to world model ySituation awareness = world model + plans/tasks

39. Situation Assessment zPerformed at multiple echelons yScouts performing reconnaissance of battlespace yC2 staff assimilates scouting and sensor reports zGeneral process: yIdentify entities yClassify groups of entities as units yDetermine units’ functionality, capabilities, plans, intent zTechnical Issues yPilot awareness and information overload ySituation assessment techniques

40. Pilot Situation Awareness zSynthetic worlds are information rich y100’s of other entities yVehicle instruments yTerrain, weather, buildings, etc. yCommunications (messages) yAmount of information will continue to increase …. zPerceive, understand, decide and act yComprehend dynamic, complex situations yDecide what to do next yDo it!

41. Information Overload

42. Roots of the Problem zNaïve vision model yEntity-level resolution only yUnrealistic field of view (360 o, 7 km radius) zPerceptual-Cognitive imbalance yToo much perceptual processing yCognitive system needs inputs, but … yIt also needs time to respond to world events

43. Approach zCreate a focus of attention yApply attention mechanisms to entity perception initially yIncorporate filters yImplement a zoom lens model (covert attention) zStages of perceptual processing yAttention in different stages: preattentive & attentive zControl the focus of attention yGoal-driven yStimulus-driven

44. Zoom Lens Model of Attention (Eriksen & Yeh, 1985) zAttention limited in scope yMulti-resolution focus yMagnification inversely proportional to field of view zLow resolution yLarge region, encompassing more objects, fewer details yPerceive groups of entities as a coherent whole zHigh resolution ySmall region, fewer objects, more details yPerceive individual entities (e.g., tank, truck, soldier)

45. Low Resolution

46. Perceptual Grouping zPreattentive zGestalt grouping yInvoluntary yProximity-based yOther features zDynamic zVoluntary grouping K K K

47. Group Features zQuantity and composition zActivity yMoving yShooting zLocation yCenter-of-mass yBounding-box zGeometric relationships wrt pilot ySlant-range, azimuth, etc.

48. High Resolution

49. Entity Features zLocation (GCS) zSpeed zVelocity zOrientation zSlant Range zForce zObject, Object Type zVehicle Class zFunction zSense Name z Altitude z Angle Off z Target Aspect z Magnetic bearing z Heading z Status z Lateral Range z Lateral Separation z Closing Velocity z Vertical Separation

50. Control of Attention zGoal-driven control yAgent controls the focus / resolution of attention xLow resolution: Scouting groups of enemy; escorting group xHigh resolution: Search for air-defense entities; engage target ySets filters that select entities for WM zStimulus-driven control yAttention can be captured involuntarily by a visual event xMuzzle flash (luminance contrast, abrupt onset) xSudden motion (abrupt onset)

51. Sea Land Overwatch Position Overwatch Position Transport Carrier Escort Carrier Rendezvous Point Escort task Orient on group Voluntary grouping Goal-driven Attention

52. Low Resolution High Resolution Stimulus-driven Attention

53. Situation Awareness at Higher Echelons Command Entity Situation Reports

54. Situation Assessment zIdentify entities yFuse scouting reports zClassify groups of entities as units yCluster entities into unit-sized groups yClassify units into functional types zDetermine capabilities, plans, intent

55. Clustering and Classification zBottom-up and top-down approach zBottom-up clustering based on proximity yIdentify a group of entities close to each other yOther useful features: color, orientation, speed zTop-down classification based on doctrine yThreat templates yIssues: which template, partial matching

56. Bottom-up Clustering zHierarchical Clustering yPartitioning starting at the top until a satisfactory level (e.g. individual units) zRobust Clustering yNearest-neighbor using center of mass xWorks well for hierarchical clustering xRequires a parameter of minimal distance yDensity-based clustering xWorks well on different shapes of patterns xNo parameter is required (or can be learned)

57. Top-Down Classification zClassification and prediction yClassification based on threat templates xDoctrine of situations, actions, formation and capacities xMatching clustered units with templates for classification yPartial matching to predict the location of missing units zEncoding threat templates yEncoding spatial information for symbolic processing xkD-tree to encode spatial relationships yAdding possible actions to nodes (units)

58. Future Situation Awareness zModel how tactical intelligence influences planning zFuture situation: knowledge goals yWhat will I need to know for this plan to work? yEstablish Priority Intelligence Requirements (PIR) xWhat commander needs to know about opposing force yDrives the placement of sensors and observation posts yConstrains the pace of plan execution zRarely addressed in current C 2 models

59. Intelligence Critical for Realistic C 2 zClose interplay between intelligence and COA Development xIntelligence guides COA development xCOA development drives intelligence needs xIntelligence availability constrains actions Some COA must be abandoned if one can’t gather adequate intelligence

60. Intelligence Critical for Realistic C 2 zIntelligence imposes temporal constraints xWhen can a satellite observe? xHow long to insert surveillance (LRSU)? xHow long before I must commit to COA?

61. Intelligence critical for realistic C 2 zIntelligence collection must be focused yCommanders must: xPrioritize their intelligence needs xUnderstand higher-level intelligence priorities xProvide intelligence guidance to subordinates e.g. Simulation Information Filtering Tool [Stone et. al]

62. Brigade Planning (simplified) yIdentify Engagement Area (EA Pad) Should canalize OPFOR and restrict movement yIdentify launch time Require 2-hour notice EA Pad AA Lincoln zAttack 2 nd echelon tank division (TD)

63. PL ECHO Brigade PIR yWhen will TD leave AA Lincoln? Verifies enemy intent yWhen will TD reach PL Echo? Satisfies the need for 2-hour notice Further verifies enemy intent Location of PL Echo driven by PIR EA Pad AA Lincoln 2hrs

64. EA Pad PL ECHO Intelligence Plan SLAR Monitor movement from assembly area LRSU Trigger attack: TD 2hrs from EA Pad Assembly Area

65. Final Brigade Plan Execute Mission Arrive at EA Break Contact Decision Point H H+2 H+3H-8 H-10 Insert LRSULRSU monitor PL Echo Deep Attack SLAR monitor AA

66. Automating PIR zIdentify PIR in my own plans yFind preconditions, assumptions, and triggering conditions that are dependent on OPFOR behavior zExtract PIR from higher echelon orders ySpecialize as appropriate for my areas of operation zDerive tasks for satisfying PIR ySensor placement zEnsure consistency of augmented plans

67. Identifying PIR zExamine COA dependencies on OPFOR ye.g. Precondition of engaging: OPFOR will-be-at EA Pad at time H+2 zLook for dependencies that: yAre not under my direct control yAre uncertain z Implemented with PIR recognition schema: yAbstract rules that scan plans and assert PIR xSome domain-independent, some domain-specific

68. Interpreting Higher Level Guidance zNeed to convert into PIR at my echelon ye.g. Brigade’s PIR: xWhen will lead regiment reach forward defense becomes Battalion PIR xWhen will lead battalion of lead regiment reach fwd def zImplemented by specialization rules yEncode doctrinal and terrain relationships

69. Deriving Sensor Plans zImplemented via tactical planning mechanism yPIR represented as “knowledge goals” yDomain theory augmented with sensing tasks xSensing tasks achieve knowledge goals xTasks encode maneuver / temporal dependencies yPlanning process fills in details xSensing tasks added to achieve knowledge goals e.g. Observe TD activity near PL_ECHO xOther tasks added to satisfy maneuver dependencies e.g. Use UH-60 to insert LRSU near PL_ECHO

70. Ensuring Consistency zImplemented via tactical planning mechanism yIf PIR goals cannot be satisfied, COA is invalid or Use unsatisfied PIR to request external assets zSensing plans constrain timing of events yIf temporal constraints inconsistent, COA is invalid

71. Significant Results

72. zContinuous planning paradigm works well for modeling C2 behavior in the joint synthetic battlespaces yDynamic planning, monitoring, and execution yHandles situation interrupts in test cases zCollaborative planning is made possible by adding a few extensions to a general purpose planner z A model of perceptual attention and situation awareness implemented in RWA-Soar pilot zDeveloped a technique for deriving Priority Intelligence Requirements with planner

73. Significant Results (2) zPublications yContinuous Planning and Collaboration for Command and Control in Joint Synthetic Battlespaces, CGF&BR ‘99 yDeriving Priority Intelligence Requirements for Synthetic Command Entities, CGF&BR ‘99 yModeling Perceptual Attention in Virtual Humans, CGF&BR ‘99 yPerceptual Grouping and Visual Attention in a Multi-agent World, Agents ‘99

74. Scope of Task Coverage ATKHB Attack Mission Achieve Tactical Disposition Reduce Enemy Posture Achieve Culminating Task Consolidate 1-4-1101: Personnel (S1) planning (C 2 ) 1-4-1201: Intelligence (S2) planning (C 2 ) 1-4-1301: Operations (S3) planning (C 2 ) 1-4-1401: Logistics (S4) planning (C 2 ) 1-4-1302: Establish and maintain tactical operations center (C 2 ) 1-4-1305: Coordinate maneuver with CSS and rear ops (C 2 ) --------------------------------------------------- 1-2-0320: Provide supply support (CSS) 1-2-7723: Perform maintenance (CSS) 1-2-7728: Process ammo and fuel (CSS) 1-4-1103: Replacement operations (CSS) 1-4-1402: Coordinate supply/equip. (CSS) 1-4-1405: Plan and coordinate transport assets (CSS) Achieve Readiness 1-3:0001: Plan and organize move (Mnv) 1-2-0101: Move to and occupy assembly area (Mnv) 1-4-1306: Establish and maintain tactical command post (C 2 ) 1-2-7726: Conduct FARP operations (CSS) Achieve Physical Posture 1-4-1305 (Section 6.1.2): Integrate fire support Attack (METL task)1-4-1206: 1-2-xxxx: Establish satellite comm. (C 2 ) 1-2-xxx0: Establish ground comm (C 2 ) 1-2-7509: Establish voice comm (C 2 ) 11-5-0104: Establish FM radio (C 2 ) 1-4-1001: Perform C 2 operations (C 2 ) 1-4-1303: Control tactical operations (C 2 ) ------------------------------------------------------------ 1-4-1202: Implement security measures (Int) 1-4-1203: Process intelligence information (Int) 1-4-1311: Liaison operations (Int) ------------------------------------------------------------ 1-4-1105: Provide admin services (CSS) 1-2-7708: Provide food support (CSS) 1-2-7710: Operate field mess (CSS) 1-2-7720: Establish med support (CSS) 1-2-7721: Conduct med activities (CSS) 1-4-1102: Perform strength management (CSS) 1-4-1104: Conduct casualty reporting (CSS) 1-4-1308: Direct army airspace C 2 (CSS) 1-4-1310: Civil-military operations (CSS) 1-4-1403: Monitor equipment readiness (CSS) 1-4-1406: Provide logistic services (CSS) Continuous Tasks Legend Implemented Partially implemented Desire to implement Less relevant

75. Expected Results zDetailed evaluation of planner yEmpirical yAnalytical zExtended model of situation awareness at entity and C2 levels yAttention, hierarchical clustering, classification, fusion zExtended model of collaboration zAbstract technical description of planner zJournal articles and conference papers

76. Measures of Success zCollective Measure yAbility of a group of entities (RWA Battalion) to achieve mission objectives in scenarios containing a wide range of situation interrupts zIndividual Measures yScalability: size of groups that can act autonomously yFlexibility: classes of situation interrupts handled by group behavior yTypes of multi-agent reasoning integrated into framework xi.e., collaborative, adversarial, temporal,... yBreadth and depth of domain knowledge xe.g., # of tasks, echelon levels, functional categories (battlefield operating systems)

77. Evaluation zEmpirical yDeveloped scenario generator, logging function yWill collect data from scenarios run in batch mode yEncode additional domain knowledge (WARSIM?) yEvaluate scalability zAnalytical yDevelop abstract description of planner yComplexity measures for scalability yAnalyze properties of collaborative planner -- can it be de- coupled from Soar-CFOR implementation?

78. Technology Transition

79. Efforts zFormulated concept for C2 in NASM zDemo at JPMR in February ‘99 zPresented 3 papers at CGF&BR, May ‘99 yPerceptual attention, C2 Modeling, PIR zJSIMS/ASTT workshop, May ‘99 yWARSIM commonality (POC’s: Milks & Karr) yONESAF?

80. Problem Areas

81. Focused Efforts Required zNot yet addressing role of learning zNeed good evaluation yScalability, robustness, efficiency, …

82. Programmatic Issues

83. Schedule zMilestone 4: 12/98 yDesign Review 2 xApproach to learning improved group models xApproach to temporal planning

84. Schedule (2) zMilestone 5: 9/99 (revise to 12/99?) yTechnology POP Demonstration 3 xRWA Attack Battalion xDemonstrate advanced group understanding xDemonstrate more advanced group planning Temporal planning Group understanding: plan recognition xDemonstrate advanced group execution Commander utilizes teamwork model (scaled down) xDemonstrate group learning Improve group models through experience yDeliver software and domain independent descriptions of new capabilities

85. Demonstration

86. Demonstration Scenario zAttack Helicopter Battalion (AH-64) yBattalion Commander y3 Helicopter Companies xCompany Commanders xApache Pilots y1 Combat Service Support Commander zDeep Attack Mission Scenario yCompanies move from Assembly Area to Holding Area ySituation interrupt: unexpected enemy forces in Holding Area yDynamically re-plan and execute mission