Program Overview for BRIMS

U.S. Army
Research Laboratory
Human Research &
Engineering Directorate
Program
Overview for
BRIMS
Dr. Laurel Allender
410-278-6233
[email protected]
Outline
•
•
•
•
ARL-HRED Organization
Key R&D Thrusts
Tools & Modeling Research
Opportunities
Army S&T Performing Organizations
Personnel
G-1
Medical
Materiel
Infrastructure/
Environmental
Medical
Medical
Command
Command
Army
Army Materiel
Materiel
Command
Command
U.S.
U.S. Army
Army Corps
Corps
of
of Engineers
Engineers
MEDCOM
AMC
USACE
Research,
Research,Development
Development
and
andEngineering
Engineering
Command
Command
Strategic
Defense
Strategic
Strategic Missile
Missile
Defense
Command
Defense Command
SMDC
Robin Keesee
Deputy to the CG
RDECOM
effective 6 Mar 05
Army
Army
Research
Research
Laboratory
Laboratory
Edgewood
Edgewood
Chem-Bio
Chem-Bio
Center
Center
Natick
NatickSoldier
Soldier
Center
Center
Communications
Communications
and
andElectronics
Electronics
RDEC
RDEC
TankTankAutomotive
Automotive
RDEC
RDEC
Armament
Armament
RDEC
RDEC
Aviation
Aviation
and
andMissile
Missile
RDEC
RDEC
Army
ArmyMateriel
Materiel
Systems
Systems
Analysis
Analysis
Agency
Agency
ARL
ARL
ECBC
ECBC
NSC
NSC
CERDEC
CERDEC
TARDEC
TARDEC
ARDEC
ARDEC
AMRDEC
AMRDEC
AMSAA
AMSAA
Underpinning Science, Technology, and Analysis
Science
Technology
Analysis
6.1
6.2
6.6
Mission
Human Research & Engineering Directorate
Laboratory & Field Experimentation
Basic and Applied Research
Conduct broad-based program of
scientific research and technology
directed toward optimizing soldier
performance and soldier-machine
interactions to maximize battlefield
effectiveness.
Improved
Performance
Research
Integration Tool
Modeling & Simulation
Analysis
Provide the Army and ARL with human
factors leadership to ensure that soldier
performance requirements are adequately
considered in technology development
and system design.
MANPRINT Analysis
ARL-HRED Offices
Human Research and Engineering Directorate
TACOM
Warren, MI
ARDEC
Picatinny Arsenal, NJ
NSC
Natick, MA
MANSCEN
Ft Leonard Wood, MO
Colorado Springs FE
Colorado Springs, CO
CAC
Ft Leavenworth, KS
USAICS
Ft Huachuca, AZ
USAFAS
Ft Sill, OK
ARL, HRED, SPD,
IMB, ODE
APG, MD
ARMC&S
Ft Knox, KY
ATEC & INSCOM
Alexandria, VA
CECOM R&DC
Ft Belvoir, VA
USADASCH
Ft Bliss, TX
JF-COM
Norfolk, VA
OTC
Ft Hood, TX
USASOC
Ft Bragg, NC
AMEDD
Ft. Sam Houston, TX
AMC FAST
--Italy
--III Corps
CERDEC
Ft Monmouth, NJ
AMCOM-MSL
Redstone Arsenal, AL
AMCOM-AVN
Redstone Arsenal, AL
SC&FG
Ft Gordon, GA
AVNC
Ft Rucker, AL
USAIC
Ft Benning, GA
STTC
Orlando, FL
JUN 02
Key R&D Thrusts
Understanding &
Augmenting Cognition
• Basic research
• Multi-tasking
• Attention & cognitive
workload
• Performance under
stress
Human Robot Interaction
• Teamwork
• Scalable displays
• Direct link to technology
development
Decision Making for C2
• Cognitive & computer
science
• Measures & models for
macro cognition
• Decision architectures
on the networked
battlefield
Situational Understanding
• Future Force Warrior
• Information to the Soldier
• Multimodal displays
M&S: Tools & Research
The Tools
M&S Research
• IMPRINT
• Cognition and
decision making
• Stressors and
performance shaping
factors
• “Ease-of-use”
• Linking models
– 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
Target-Present
ACT-R
Before Window
Radio Window
20 seconds – Rhythmic Condition
10-30 seconds – Varied Condition
10 seconds – All
Conditions
Targe
t 4-6
sec.
• The effect of timing on
performance
Target-Absent
• Modeling diagrammatic
reasoning
• Multi-tasking
Targe
t 4-6
sec.
Targe
t 4-6
sec.
To
ne
Before Window
Radio Window
20 seconds – Rhythmic Condition
10-30 seconds – Varied Condition
10 seconds – All
Conditions
Targe
t 4-6
sec.
• Cognitive Robotics
Targe
t 4-6
sec.
Obstacles
Goal
Targe
t 4-6
sec.
Targe
t 4-6
sec.
Targe
t 4-6
sec.
To
ne
Enemy
Location
Robot
from Chandrasekaran, Josephson, Banerjee, Kurup, & Winkler
Modeling Coalition Teamwork in
Effects Based Operations Extending C3TRACE:
Process
Organization
Technology
The EBO Process
Knowledge Base
Development
Network 0 Untitled
KB operational ISR-products and
Analyses from CoE
Staff
SME
1755
HPTS
Effects-based
Planning
Effects-based
Planning
JIAI
National
knowledge
X 10 min
EBE
EBA
EBP
BLUE
1761
Evaluate Intel
X 10 min
Effects-based
Execution
1763
Collaborate with
staff
X 15 min
1757 International,
HPTS Recomendation
Coalition,
Alliance
Agreements
M
18 min
X
1762
Evaluate Collection
Effects-based
Assessment
IPB
MN
knowledge
International
Laws
Effects-based
Execution
MNIG
RA
SoSA
X 10 min
Effects-based
Assessment
1759
AGM
X 20 min
Higher Guidance
& Intent
1764
National
D - How to Adjust
Policies
Plan
& Strategies
Decision
Decision
Enemy
Who
Enemy
What
Enemy
Where
Enemy
When
Friendly
Who
Friendly
What
Friendly
Where
Friendly
When
Friendly
How
Time
Since
Update
(min)
10
2
10
10
10
10
10
10
0
Frequency
/Volatility
Category
D
D
A
A
D
D
A
A
D
Decay
Rate (%
per min)
1
1
5
5
1
1
5
5
1
Info
Quality
(%)
90
98
50
50
90
90
50
50
100
Average
Info
Quality
(%)
74.2
time
The Impact of Culture on Coalition Teamwork
Cultural Factors
Independent v. Interdependent
Risk Tolerant v. Risk Averse
Impact Teamwork
Information Sharing
Decision Making
Negotiation
Egalitarianism v. Status
Communication
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.
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
508
Dynamic
Re-tasking
501
Flight
M
142
600
502
Search
Target
P
504
Detect
Target
503
Monitor
AV
T
505
Inflight
T
Modificati
• Embedding intelligent agents in battlefield
systems to assist Soldiers in their real-time
decision making
506
Target
Exploitati
510
Icing
520
Generator
Failure
530
Signal
Degradation
Intermittent
Link Loss
T
507
Flight
540
Payload
Failure
550
AVO/MPO
Console
Fails
560
GPS
Failure
Stressors & Performance Shaping
Factors
IMPRINT
Vibration & thermal - FY04
Vigilance, training, time, team - FY05
DoD benchmarked stressors
C3TRACE
State stressors – e.g., self efficacy
Making Modeling Easier
“Standard”
• Streamline tool
functionality
“Pro”
• A graphical interface specification that
creates a hybrid
ACT-R / task
network model
Linking Models for Systems of
Systems Modeling
Combined Arms Mission
• 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.
Phase 5 – Assault of an Enemy Position
1st Plt UAV identifies vehicle east
of bridge as red armor target
SUGV Operator moves S UGV
further E ast along Route Bama
M CS VC M onitors
mission C OP for SA
ARV-A in
Support by Fire
Position
A Tm crosses Raccoon Creek &
establishes support by fire position
3 rd Plt ARV-R Acoustic Sensors
detect vehicle east of bridge
B Tm Crosses Raccoon
Creek to Assault Position
N
W
E
1st Sqd ICV
S
Mounted & Dismounted Model
Infantry Squad with Unmanned
Assets (SQ & PLT)
Architecturethat
that
Architecture
Integrates
Integrates
IndividualIMPRINT
IMPRINT
Individual
Models
Models
(MCS.INF,RAVEN,
RAVEN,
(MCS.INF,
ARV-R,
ARV-R,
etc)Into
IntoCommon
Common
etc)
Simulation
Simulation
Mobile Combat System (MCS)
Platoon with Unmanned
Assets (PLT)
MATREX Conceptual Framework
III.C4.2003.05 Modeling Architecture for Technology Research &
Experimentation
Linked model representations • Observable environment features terrain & weather
• Entities - tanks, helicopters
Linked
model
• Aggregate
level -representations
units
• Sensors,
C3 network & messages
• Observable environment
features - terrain & weather
• And human performance • Entities - tanks,
– Provides MATREX more
helicopters, soldiers
realistic timing of C3 traffic
(incorporates human delays)
• Aggregate level - units &
– Provides human performance
forces
model (IMPRINT) more
realistic communications load
• Sensors
for human workload metrics
• C3 network & messages
Maneuver Commander
IMPRINT Model
4
Evaluate
Need to Issue
Report
3
Evaluate
Need to Issue
Command
Report
Order
1
Process
In-coming
C2
Command
Formation
Bad
Monitor
Situation
New Order
Reports
HLA
RTI
Orders
Routes
Report
Queue
Command
Queue
MATREX C3Grid
Issue Command
Op. Activity
Report Needed
Cmd Reviewed
Cmd Available
Rpt Available
Rpt Reviewed
Monitor
External
Communications
Maneuver Behaviors:
-Correlate Forces (COFM)
-Select Operational Activity
-Request routes
-Assess Unit Formation
-Issue Commands & Reports
Behaviors Federate
6
Issue
Report
Time
Issue Report
New Report
5
Issue
Command
Formation Status
2
Process
In-coming
C2
Report
Reports
Orders
Route Req.
HLA
RTI
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.
Technical Program
Advanced Decision Architectures
Collaborative Technology Alliance
Consortium Partners
„
„
„
„
„
„
„
„
„
„
„
„
„
Micro Analysis & Design,
Inc. (Lead)
Klein Associates
SA Technologies
ArtisTech, Inc.
Ohio State University
New Mexico State
University
University of West
Florida, Institute for
Human and Machine
Cognition
Massachusetts Institute
of Technology
Carnegie Mellon
University
University of Central
Florida
University of Maryland
University of Michigan
Wright State University
Objectives
To work together to develop,
test, and transition new user-
Technical Areas
„
Cognitive Process
Modeling and
Measurement
„
Analytical Tools for
Collaborative Planning
and Execution
„
User-Adaptable
Interfaces
„
Auto Adaptive
Information
Presentation
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.
CTA Annual Conference
1-3 June
Arlington, VA
6.1 Basic Research
IV.C4.2003.03 Command & Control in
Complex & Urban Terrain (C2CUT) ATO
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.
Small Robots
Collaborative Technologies
TRLs
Actual system
"flight proven"
Field Experiments
with Evaluation
2006
2005
2004
2D/3D Battlefield
Visualization
2003
O
ST RT
Tactical Weather Decision Aids
A
2002 ST
2001
Actual system "flight
qualified"
System prototype demonstration in an
operational environment.
System /subsystem model or prototype
demonstration in a relevant environment.
Component / breadboard validation in relevant
environment.
Component / breadboard validation in laboratory environment.
Analytical and experimental critical function / proof of concept.
Technology concept and /or application formulated.
Basic principles observed and reported.
Advanced Displays Fed Lab
O
2007 ST D
EN
Situational Understanding (SU) as an
Enabler for Unit of Action Maneuver
Team Soldiers ATO
FY03
FY04
FY06
FY05
Area
1.
CIRs
IMPRINT workload
& display options
C3TRACE models
Sim & testbed development
2.
& early experiments
3.
Part task
experiments
Literature searches, icon
studies, haptic studies
FoF Model
exploration
Integrated
experiments
Predictions
Target Audience Soldier Studies
4.
5.
Model updates
Display modality
experiments
Identify data
needed
Insert data
into FoF models
ARL-TR
ARL-TR
ARL-TR
ARL-TR
ARL-TR-XX
Display Design
Guidelines for
FFW and FCS
Technology for Human-Robot Interaction
(HRI) Soldier Robot Teaming ATO
III.C4.2004.04
A joint effort to develop a
common user interface
that maximizes multifunctional 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
TRL
6
Simulation
Advanced
concepts
TRL-4-5
Experimentation
OCU concepts &
adaptive logics
TRL-2-3
Modeling
Soldier missions for robotic
vignettes – FCS and FFW
SRL- 2-3
Roadmap – Technology for HRI
Soldier Robot Teaming ATO
FY04
FY05
Initial
Models
FY06
FY07
Modeling
Field
Final
System of
data
Models
System models
FY08
Workload & Cognitive
Models for FCS and
FFW robotic ops
Simulator
Crew Issues
Workload &
Crew size
Display
Crew function
effects
TARDEC Simulator
Validation
Automation
Initial
Sim.
Task
study
Experiments
CTA Robotic
Architecture
Auto
Logic
Experiments,
Final Taxonomy
Operator Control Unit
Small robots control, Stereo-Vision
Multi-modality experiments
Products
Prototype
Validations
Teaming Research
TARDEC Intelligent Agent Workload Reduction Software
TARDEC Simulations, Demos, & Development of Scalable OCUs
Crew issues for mounted
control of UAV and UGV
systems
Logic for Intelligent
Agent Allocation-
Principles and
Requirements for
Scalable OCUsHRI teams:
Training &Collaboration
Technologies
ARL
-TARDEC
IV.MS.2005.04 Enhanced Learning
Environment with Creative Technologies
(ELECT) 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:
¾STTC
¾ARI
¾ARL-HRED
¾ICT
¾ARL-HRED
¾Develop cognitive task
analysis & metrics for cognitive
and technological readiness;
evaluate & consult on
immersion interface designs
• 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
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.
FY05
TRL=3
Current Level
METRICS:
Training scenarios can’t
capture COE lessons
No auto-coaching/mentoring
Training module development
time is 18-24 months
Training retention and
transfer are indeterminate
Pacing Technologies:
Authoring and
Coaching Tools
Learning Model/
Learner Technology
Readiness Metrics
Soldier Performance
and Cognitive
Readiness Metrics
FY06
FY07
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 technology
readiness metrics, pedagogical
design, initial learning model, and
initial training effectiveness
metrics; assess effectiveness of
existing comparable single user
training module
FY08
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
HRED—Develop cognitive task
analysis and metrics for
HRED—Working with STTC & ICT,
cognitive and technologicaldevelop scenario task analysis;
develop cognitive task analysis
readiness; evaluate and consult
for multi-player training module
on immersion interface designs
TRL=6
STTC & ICT — Incorporate lessons
learned with new tools and
methods to reduce development
time and costs; transition multiuser module, tools and methods
ARI — Assess effectiveness of multiuser 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:
METRICS:
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
Can modify 50% of training module to
tailor training for multi-users
Cognitive 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
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 textbased instruction baseline
Learning 30% better than baseline