U.S. ArmyResearch LaboratoryHuman Research & Engineering Directorate

Program Overview for BRIMS

Dr. Laurel Allender410-278-6233lallende@arl.army.mil

Outline

• ARL-HRED Organization• Key R&D Thrusts• Tools & Modeling Research• Opportunities

Army S&T Performing OrganizationsArmy S&T Performing Organizations

MaterielPersonnel MedicalInfrastructure/Environmental

Strategic Defense

Army MaterielCommand

Army MaterielCommand

AMCAMC

Underpinning Science, Technology, and AnalysisUnderpinning Science, Technology, and Analysis

Research, Developmentand Engineering

Command

Research, Developmentand Engineering

Command

RDECOMRDECOM

G-1G-1MEDCOMMEDCOM

Medical CommandMedical

CommandU.S. Army Corps

of EngineersU.S. Army Corps

of Engineers

USACEUSACEStrategic Missile

Defense CommandStrategic Missile

Defense Command

SMDCSMDC

Army MaterielSystems AnalysisAgency

AMSAA

Army MaterielSystems AnalysisAgency

AMSAA

ArmyResearch

Laboratory

ARL

ArmyResearch

Laboratory

ARL

EdgewoodChem-Bio

Center

ECBC

EdgewoodChem-Bio

Center

ECBC

Natick SoldierCenter

NSC

Natick SoldierCenter

NSC

Communicationsand Electronics

RDEC

CERDEC

Communicationsand Electronics

RDEC

CERDEC

Tank-Automotive

RDEC

TARDEC

Tank-Automotive

RDEC

TARDEC

ArmamentRDEC

ARDEC

ArmamentRDEC

ARDEC

Aviationand Missile

RDEC

AMRDEC

Aviationand Missile

RDEC

AMRDEC

Analysis6.6

Technology6.2

Science6.1

Robin Keesee Deputy to the CG

effective 6 Mar 05

Conduct broad-based program of scientific research and technology directed toward optimizing soldier performance and soldier-machine interactions to maximize battlefield effectiveness.

Provide the Army and ARL with human factors leadership to ensure that soldier performance requirements are adequately considered in technology development and system design.

MissionHuman Research & Engineering Directorate

Basic and Applied Research

Analysis

Improved Performance ResearchIntegration Tool

Laboratory & Field Experimentation

Modeling & Simulation

MANPRINT Analysis

ARL-HRED OfficesHuman Research and Engineering Directorate

CERDECFt Monmouth, NJ

NSCNatick, MA

ARL, HRED, SPD,IMB, ODEAPG, MD

ATEC & INSCOMAlexandria, VA

CECOM R&DCFt Belvoir, VA

USASOCFt Bragg, NC

SC&FGFt Gordon, GA

STTCOrlando, FL

USAICFt Benning, GA

AVNCFt Rucker, AL

AMCOM-MSLRedstone Arsenal, AL

AMCOM-AVN Redstone Arsenal, AL

ARDECPicatinny Arsenal, NJ

TACOMWarren, MI

MANSCENFt Leonard Wood, MO

CACFt Leavenworth, KS

USAICS Ft Huachuca, AZ

ARMC&SFt Knox, KY

OTCFt Hood, TX

USAFASFt Sill, OK

USADASCHFt Bliss, TX

JUN 02

AMC FAST--Italy

--III Corps

AMEDDFt. Sam Houston, TX

Colorado Springs FEColorado Springs, CO

JF-COM Norfolk, VA

Key R&D Thrusts

Decision Making for C2Decision Making for C2•• Cognitive & computer Cognitive & computer sciencescience•• Measures & models for Measures & models for macro cognitionmacro cognition•• Decision architectures Decision architectures on the networked on the networked battlefieldbattlefield

Human Robot InteractionHuman Robot Interaction•• TeamworkTeamwork•• Scalable displaysScalable displays•• Direct link to technology Direct link to technology developmentdevelopment

Situational UnderstandingSituational Understanding•• Future Force Warrior Future Force Warrior •• Information to the SoldierInformation to the Soldier•• Multimodal displaysMultimodal displays

Understanding &Understanding &Augmenting CognitionAugmenting Cognition•• Basic researchBasic research•• MultiMulti--tasking tasking •• Attention & cognitive Attention & cognitive workloadworkload•• Performance under Performance under stressstress

M&S: Tools & Research

M&S Research

• Cognition and decision making

• Stressors and performance shaping factors

• “Ease-of-use”• Linking models

The Tools

• IMPRINT– Improved Performance

Research Integration Tool

• C3TRACE– Command, Control, &

Communication: Techniques for Reliable Assessment of Concept Evaluation

• ACT-R– Adaptive Control of

Thought-Rational

Understanding & Augmenting Cognition

ACT-R

• The effect of timing on performance

• Modeling diagrammatic reasoning

• Cognitive Robotics

• Multi-tasking

Before Window

20 seconds – Rhythmic Condition10-30 seconds – Varied Condition

Radio Window

10 seconds – All Conditions

Tone

Target 4-6 sec.

Before Window

20 seconds – Rhythmic Condition10-30 seconds – Varied Condition

Radio Window

10 seconds – All Conditions

Target-Present

Target-Absent

Tone

Target 4-6 sec.

Target 4-6 sec.

Target 4-6 sec.

Target 4-6 sec.

Target 4-6 sec.

Target 4-6 sec.

Target 4-6 sec.

Goal

EnemyLocation

Robot

Obstacles

from Chandrasekaran, Josephson, Banerjee, Kurup, & Winkler

Knowledge BaseDevelopment

Effects-basedPlanning

Effects-basedExecution

Effects-basedAssessment

Effects-basedPlanning

Effects-basedExecution

time

Effects-basedAssessment

The Impact of Culture on Coalition Teamwork

ProcessOrganizationTechnology

74.2Average

Info Quality

(%)

1005050909050509890Info

Quality(%)

155115511Decay

Rate (% per min)

DAADDAADDFrequency/Volatility Category

0101010101010210

Time Since

Update (min)

Friendly How

Friendly When

Friendly Where

Friendly What

Friendly Who

Enemy When

Enemy Where

Enemy What

Enemy WhoDecision

74.2Average

Info Quality

(%)

1005050909050509890Info

Quality(%)

155115511Decay

Rate (% per min)

DAADDAADDFrequency/Volatility Category

0101010101010210

Time Since

Update (min)

Friendly How

Friendly When

Friendly Where

Friendly What

Friendly Who

Enemy When

Enemy Where

Enemy What

Enemy WhoDecision

Network 0 Untitled

1755HPTS

1757HPTS Recomendation

1759AGM

1761Evaluate Intel

1762Evaluate Collection

1763Collaborate with staff

1764D - How to Adjust PlanM

10 min

10 min

10 min

15 min 18 min

20 min

DecisionX

X

XX

X

X

International,Coalition,Alliance

Agreements

NationalPolicies

& Strategies

InternationalLaws

Higher Guidance& Intent

EBP EBE EBA

Nationalknowledge SoSA MN

knowledge

SME JIAI RA MNIG IPB

KB operationalStaff

ISR-products andAnalyses from CoE

BLUE

The EBO Process

Cultural impacts on teamwork will be included in the model through careful construction of communication events and through the flow of communications through the organizational and process structures.

Cultural Factors Impact Teamwork

Independent v. Interdependent

Risk Tolerant v. Risk Averse

Egalitarianism v. Status

Information Sharing

Decision Making

Negotiation

Communication

Modeling Coalition Teamwork in Effects Based Operations -Extending C3TRACE:

142

501Flight

502Search Target

503Monitor AV

504Detect Target

505Inflight Modificati

506Target Exploitati

507Flight

508Dynamic Re-tasking

510Icing

520Generator Failure

530

540Payload Failure

550

560GPS Failure

600

M P

T T

T

AVO/MPOConsoleFails

SignalDegradationIntermittentLink Loss

Using Models of Recognition Primed Decision Making for Prediction, Analysis, & Aiding

• Decision modeling for a network-centric battlefield simulation - exploit complementary relationship between two M&S environments– A network model that provides rich, constructive

simulation of the UAV and its environment, but a comparatively abstract representation of the human control of the UAV

– Task network models of UAV control provide a detailed model of the human operator, but a comparatively abstract representation of the operator’s environment

• Embedding intelligent agents in battlefield systems to assist Soldiers in their real-time decision making

Stressors & Performance Shaping Factors

Vibration & thermal - FY04

Vigilance, training, time, team - FY05

DoD benchmarked stressors

IMPRINT

State stressors – e.g., self efficacy

C3TRACE

“Standard”

Making Modeling Easier

“Pro”

• Streamline tool functionality

• A graphical interface specification that creates a hybrid ACT-R / task network model

Mounted & Dismounted Model Infantry Squad with Unmanned

Assets (SQ & PLT)

Mobile Combat System (MCS)Platoon with Unmanned

Assets (PLT)

B Tm Crosses Raccoon Creek to Assault Position

1st Plt UAV identifies vehicle east of bridge as red armor target

A Tm crosses Raccoon Creek & establishes support by fire position

SUGV Operator moves SUGV further East along Route Bama

Phase 5 – Assault of an Enemy Position

ARV-A in Support by Fire Position

N

W

S

E1st Sqd ICV

MCS VC Monitors mission COP for SA

3rd Plt ARV-R Acoustic Sensors detect vehicle east of bridge

• INF PLT use CL I UAV for route recon.• INF SQ use SUGV for red target detection at danger area.• MCS use ARV-R acoustic sensors to detect BLOS red armor target.• Both INF & MCS use CL I UAV to conduct BDA of red armor targets and update both COPS.

Combined Arms Mission

Architecture that Integrates

Individual IMPRINT Models

(MCS.INF, RAVEN, ARV-R,

etc) Into Common Simulation

Architecture that Integrates

Individual IMPRINT Models

(MCS.INF, RAVEN, ARV-R,

etc) Into Common Simulation

Linking Models for Systems of Systems Modeling

Linked model representations -

• Observable environment features - terrain & weather

• Entities - tanks, helicopters, soldiers

• Aggregate level - units & forces

• Sensors• C3 network & messages

MATREX Conceptual FrameworkIII.C4.2003.05 Modeling Architecture for Technology Research & Experimentation

Linked model representations -• Observable environment features -

terrain & weather• Entities - tanks, helicopters• Aggregate level - units • Sensors, C3 network & messages

MonitorExternal

Communications

2Process In-coming

C2Report

1Process In-coming

C2Command

4Evaluate

Need to IssueReport

3Evaluate

Need to IssueCommand

5Issue

Command

6Issue Report

MonitorSituation

ReportQueue

CommandQueue

Maneuver Behaviors:-Correlate Forces (COFM)-Select Operational Activity-Request routes-Assess Unit Formation-Issue Commands & Reports

HLARTI

HLARTI

Reports

Orders

Routes

Reports

Orders

Route Req.

Rpt

Ava

ilabl

e

Rpt

Revi

ewed

Cmd

Ava

ilabl

e

Cmd

Revi

ewed

Form

atio

n St

atus

New Report New Order FormationBad

Op.

A

ctiv

ity

Repo

rt N

eede

d

Time

Issu

e Co

mm

and

Issu

e Re

port

Order

Report

Maneuver CommanderIMPRINT Model

MATREX C3Grid Behaviors Federate

• And human performance– Provides MATREX more

realistic timing of C3 traffic (incorporates human delays)

– Provides human performance model (IMPRINT) more realistic communications load for human workload metrics

Opportunities

• Cross-directorate collaboration in ARL• New US-UK Alliance in “Network

Science”• BRIMS Connections!

Back-up Slides

Augmenting MATREX

• Phase I SBIRs, Phase II invited• Charles River Analytics & DCS

– Title: Command Decision Modeling in Distributed Combat Simulation

– Objectives:• To provide an asymmetric, non-scripted, adaptive model

of battlefield decision-making to the C3Grid of the MATREX distributed simulation environment.

• To improve the representation of decision making in combat simulations so that it accurately reflects aided, automated, and human processing of information and it’s impact on tactical decision-making.

ObjectivesObjectives

Consortium PartnersConsortium Partners

Micro Analysis & Design, Inc. (Lead)Klein AssociatesSA TechnologiesArtisTech, Inc.Ohio State UniversityNew Mexico State UniversityUniversity of West Florida, Institute for Human and Machine CognitionMassachusetts Institute of TechnologyCarnegie Mellon UniversityUniversity of Central FloridaUniversity of MarylandUniversity of MichiganWright State University

Technical AreasTechnical AreasCognitive Process Modeling and Measurement

Analytical Tools for Collaborative Planning and Execution

User-Adaptable Interfaces

Auto Adaptive Information Presentation

To work together to develop, test, and transition new user-interface technologies and computer science innovations that will facilitate better soldier understanding of the tactical situation, more thorough evaluation of courses of action, and, ultimately, better and more timely decisions.

6.1 Basic Research

Technical Program

CTA Annual Conference 1-3 June

Arlington, VA

Advanced Decision ArchitecturesCollaborative Technology Alliance

IV.C4.2003.03 Command & Control in Complex & Urban Terrain (C2CUT) ATO

Basic principles observed and reported.

Technology concept and /or application formulated.

Analytical and experimental critical function / proof of concept.

Component / breadboard validation in laboratory environment.

Component / breadboard validation in relevant environment.

System /subsystem model or prototype demonstration in a relevant environment.

System prototype demonstration in an operational environment.

Actual system "flight qualified"

Actual system "flight proven"

Goal: To provide C2 capabilities that provide Commanders and Soldiers with enhanced, networked information collection, management and decision aids to: collectively plan the battle, see first, act first, and finish decisively on a complex or urban battlefield.

2003

2001

2004

20052006

TRLsTRLs

Field Experiments with Evaluation

2002STO

START

STO

END2007

2D/3D Battlefield Visualization

Collaborative Technologies Small Robots

Tactical Weather Decision Aids

Advanced Displays Fed Lab

FY03 FY05 FY06FY04

Area

1.

2.

3.

4.

5.

CIRsIMPRINT workload& display optionsC3TRACE models

Model updates

Sim & testbed development & early experiments

Part taskexperiments

Integratedexperiments

Target Audience Soldier Studies Predictions

Literature searches, iconstudies, haptic studies

Display modality experiments

FoF Modelexploration

Identify dataneeded

Insert data into FoF models

ARL-TRARL-TR

ARL-TRARL-TR

ARL-TR-XXDisplay Design Guidelines for FFW and FCS

Situational Understanding (SU) as an Enabler for Unit of Action Maneuver Team Soldiers ATO

Technology for Human-Robot Interaction (HRI) Soldier Robot Teaming ATOIII.C4.2004.04

A joint effort to develop a common user interface that maximizes multi-functional soldier performance of primary mission tasks by minimizing required interactions and workload in the control of ground and air unmanned systems and minimizes unique training requirements

TRL6

SimulationAdvanced concepts TRL-4-5

Experimentation OCU concepts & adaptive logics

TRL-2-3Modeling

Soldier missions for robotic vignettes – FCS and FFW

SRL- 2-3

ModelingInitial

ModelsSystem of

System modelsFinal

Models

Workload & CognitiveModels for FCS and

FFW robotic ops

SimulatorCrew issues for mounted control of UAV and UGV

systems Workload &Display effects

Crew sizeCrew function TARDEC Simulator

Validation

Automation

InitialSim.Taskstudy

ExperimentsCTA RoboticArchitecture

AutoLogic

Experiments,Final Taxonomy

Operator Control Unit

Small robots control, Stereo-VisionMulti-modality experiments

PrototypeValidations

Teaming Research

FY04 FY05 FY06 FY07 FY08

TARDEC Intelligent Agent Workload Reduction Software

TARDEC Simulations, Demos, & Development of Scalable OCUs

Logic for IntelligentAgent Allocation-

Principles andRequirements forScalable OCUs-

HRI teams:Training &Collaboration

Technologies

-TARDEC

ARL

Products

Crew Issues

Field data

Roadmap – Technology for HRI Soldier Robot Teaming ATO

Overall Purpose:Incorporate Contemporary Operating Environment (COE) lessons learned into an effective, interactive, simulation training capability that can be rapidly developed, modified, and deployed.

Overall Products:• Advanced tools and methods for rapidly creating

adaptive, lower cost, interactive training simulations

• Single- and multi-user training modules

Payoff:• Enhanced, immersive, interactive training

environments, easily updated based on changes and lessons learned in the COE

• Enhanced tools and methods which increase learning & knowledge retention

• Enhanced training that can support synchronous or asynchronous, individual or collaborative, small groups

• Training modules, tools, and methods for transition to TRADOC in FY06 & 08

STTCARIARL-HREDICT

ARL-HREDDevelop cognitive task

analysis & metrics for cognitive and technological readiness; evaluate & consult on immersion interface designs

IV.MS.2005.04 Enhanced Learning Environment with Creative Technologies (ELECT) ATO

FY05 FY06 FY07 FY08

Pacing Technologies:

TRL=3

Current Level

METRICS:Training scenarios can’t

capture COE lessonsNo auto-coaching/mentoringTraining module development

time is 18-24 monthsTraining retention and

transfer are indeterminate

Authoring and Coaching Tools

Learning Model/ Learner Technology Readiness Metrics

Soldier Performance and Cognitive Readiness Metrics

TRL=4

STTC & ICT — Develop new authoring tools and coaching tools; develop single-user training module in FY06; transition module, tools, and methods at end of FY06

ARI — Develop learner technologyreadiness metrics, pedagogical design, initial learning model, and initial training effectiveness metrics; assess effectiveness of existing comparable single user training module

METRICS:Can modify 50% of training module

for learner level automatically or by option selection

Cognitive load of training is optimal as validated by cognitive readiness metrics

Auto-coaching/mentoring available for 40% of applicable portions of training module

TRL=5

STTC & ICT — Develop additional methods and tools to support multi-user training module; include synchronous training; transition tools and methods

ARI — Develop multi-player pedagogical design and learning model; develop multi-user training effectiveness metrics; assess effectiveness of FY06 single user training module

METRICS:Can modify 50% of training module to

tailor training for multi-usersCognitive load of training is optimal

among multi-users as validated by cognitive readiness metrics

Auto-coaching/mentoring available for 40% of applicable multi- user needs in training module

TRL=6

STTC & ICT — Incorporate lessons learned with new tools and methods to reduce development time and costs; transition multi-user module, tools and methods

ARI — Assess effectiveness of multi-user training module; publish guide summarizing lessons learned which describe how best to design and implement game engine based training

HRED — Assess impact of training modules on cognitive and technological readiness

METRICS:Can update module for COE lessons

in 2 weeks; can construct new scenario in 4 weeks

Learning retention 30% greater in content or 30% longer than text-based instruction baseline

Learning 30% better than baseline

HRED—Working with STTC & ICT, develop scenario task analysis; develop cognitive task analysis for multi-player training module

HRED—Develop cognitive task analysis and metrics for cognitive and technological readiness; evaluate and consult on immersion interface designs

Joint objective for the ELECT ATO is to develop the didactic design, methods, tools, and metrics for the use of interactive simulation technology that can be rapidly deployed, modified, and developed to the Future Force.