Naval Program on
Human Modeling for
Computer Generated Forces
Denise Lyons, Ph.D.
NAWCTSD, Air 4962
LyonsDM@navair.navy.mil
Harold Hawkins, Ph.D.
Office of Naval Research
HawkinH@onr.navy.mil
Fleet Requirements Identified
• Growing military concerns with Affordability and
Readiness dictate an increased role for virtual and
constructive simulations
• However actual effectiveness depends on the quality
of the simulation :
– poor M&S yields ineffective training & invalid analysis
• Blue Ribbon Panels, Senior Navy management
recommendations (DDR&E, NRC, NSB, NRAC, Wald Team)
– Navy & MC need robust technical solutions for
• Training (e.g, BFTT, JSIMS, F/18 PPT)
• Acquisition (e.g, DD-21, JSF, LPD-17, AAAV, LCAC)
• Analysis, mission planning & rehearsal (e.g, JCOS,
DMT)
• ONR-Future Naval Capabilities Enabling Technology
– Capable Manpower
– Decision Support
– Time Critical Strike
Naval (and DoD) Interests in
Cognitive Modeling
• Good predictive models of human cognition & performance needed in
military simulations for training and analysis
– Challenging simulated adversaries and intelligent team mates for
simulation-based training and mission rehearsal
– Intelligent tutors and diagnostic student models for intelligent
computer -aided instruction
– Human-like intelligence for
• Mission planning
• Human-system interface design
• Requirements identification and assessment
• Decision support
• Simulation-based acquisition
• High level control techniques for autonomous platforms
Human Modeling Thrust Targets
Shortcomings of Current CGF Technology
• Current military simulation environments rely on Semi-Automated
Forces (controller augmented) because underlying models of behavior
exhibit limited capabilities
– Behave predictably, usually according to doctrine, making them gameable
– Reactive planning absent or highly restricted
– Sensitivity to performance modulators (stress, risk aversion, fatigue,
training, fear, etc) limited, often not validated
– Situation awareness capabilities limited
– Do not generate useful self-explanation
– Many lack integrated perceptual-motor and cognitive systems
– Limited in ability to respond reasonably to unanticipated events
(robustness)
– Mechanisms for learning from experience (adaptability) lacking or limited
These are some of the shortfalls the Program aims to address
CGFs for Military Simulations:
Automated Forces vs. Semi-Automated Forces
•
Today: CGFs used as adversaries and teammates in simulations for training are stupid,
brittle, and predictable, locking us into a dilemma of cost-ineffectiveness. Either
– We train against easily defeatable fully automated adversaries, yielding ineffective training,
or
– We train with assistance of many skilled human controllers, reducing training flexibility &
significantly increasing training costs.
•
Future: Advances in soft computation & open systems architecture technology will be
exploited to provide fully automated CGFs that are realistic, cognitively competent &
challenging,, yielding training that is both effective and affordable
•
•
Payoff:
– Stand alone CGFs--smart, robust, adaptable, unpredictable, realistic, challenging
– First-time capability for realistic anytime, anywhere, on-demand simulation-based training
– Affordability: > 75% reduction in simulation manning requirements
A Strong Customer Base: N789, PMA-205; N769, PMS-430; MARCORSYSCOM, JSIMS;
BFTT; CM FNC
Tools for Scenario-Based Training
SCENARIO GENERATION
SCENARIO EXECUTION
(OPFOR/BLUFOR)
AUTOMATED PERFORMANCE
MEASUREMENT
INTELLIGENT TUTORS
REAL TIME INSTRUCTOR AIDS
ON-LINE FEEDBACK
AUTOMATED DIAGNOSIS
and DEBRIEFING
Our Research Identified Required Enabling Technologies:
• Human Behavior Modeling
• Intelligent Agents
• Computer Generated Forces
An Integrated Research Approach
6.1
HBR and CGF
architecture
development
and studies
6.2
Investigate the
feasibility of
instructional
strategies using
HBR and CGFs
6.3
Demonstrate and
measure the
effectiveness of
HBR and CGFs
in prototype
Navy & MC
Training
Simulations
6.4+
Apply
HBR and CGFs
to deployable
Navy & MC
Training
Simulations
and define
specifications for
implementing in
future platforms
Products transition forward
Requirements and research questions flow back
Defense Technology Objective (DTO) HS.30
Realistic Cognitive and Behavioral Representations in Simulation
CGF R&D Programs
& Transitions
6.1/SBIR
6.2
6.3
ONR M&S
Realistic
Human
Modeling
Computer
Generated
Forces
Synthetic
Cognition for
Operational
Team Training
(SCOTT)
Diagnostic
Utility of
Math
Modeling
Distributed
Team
Training for
MultiPlatform
Aviation
Missions
SBIR Phase II
PE0602233N
• Teammates
• JSAF
• Tutoring
Fleet Integration
Training
Evaluation
Research
(FITER)
PE0602233N
Dynamic
Assessment
PE0602233N
PE0603707N
• E-2C
• LCAC
Transportable
Strike Assault
Rehearsal
System
(TSTARS)
6.4
Air Warfare
Training
Development
Research Tasks
• Deployed Trng
Technology Eval
• Deployed Trng
Reqmts Analysis
• Deployed Aviation
Team Trng
• Intelligent
Synthetic Forces
Acquisition +
Deployable Tactical
Aviation Trng Sys
(DTATS)
BFTT, SWOS
Support ACTC:
NSAWC, Weap
Schools, Fleet Sqdns,
Air Wing Trng
FA-18 (17C-OFP)
PTT
PE0603707N
• F/18
Intelligent
Agents to
Enhance
Learning in
Large Scale
Exercises
PE0603707N
• JSIMS
Advanced
Embedded
Training (ATD)
AAAV, JSF, DD21,
LPD-17 CVNX, &
other new
construction
JSIMS, ONESAF
DMT, MCASMP
Human Modeling for CGFs:
Sampling of Current 6.1 Effort (FY00)
– ACT-R/PM provided with multi-tasking capability for more realistic performance
of complex multitask environments (AMBR ATC) composed of multiple
concurrent sub-tasks; extended learning capabilities & team modeling to be
added (Lebiere and Anderson/CMU)
– COGNET, a leading blackboard based model of human cognition, enhanced to
include both perceptual and motor system modeling, providing a significant
increase in its range of application (Zachary/CHI Systems)
– A principled analysis of key sources of brittleness in rule-based models has been
conducted--to be used to enhance robustness of Tac-Air Soar (Nielsen/Soar,
Inc)
– A mechanism to control the real-time execution of action is being added to
SOAR, enabling it to produce cognition-action sequences in the same time
frame as humans, and affected in a like way by performance moderators
(Laird/U.Mich)
– A high training value self explanation capability is being created for broad
application across rule-based cognitive architectures (Jones/Soar, Inc)
6.2 Issue: Three components of
behavior to support training
• Task component:
is required to carry out the task?
What
• Instruction/Practice component:
What are appropriate instructional
strategies?
• Diagnosis and Feedback component:
What is required to diagnose trainees’
behavior and provide feedback?
(Schaafstal)
Two 6.3 Programs….. Targeting
Both Ends of the Continuum
Category 3
Joint Task Forces Exercises
6.3 Intelligent Agents to Enhance
Learning in Large Scale Exercises
• Targeted for JSIMS
Category 1
Individual Training
6.3 Synthetic Cognition for
Operational Team Training (SCOTT)
• Deployed/Embedded training
• E2-C
• VELCAC
6.3 Intelligent Agents to Enhance Learning
in Large Scale M&S Exercises
Meeting Important Operational Requirements:
• Military Operations are Increasingly being
Performed by Joint Task Forces (JTF)
• Few Opportunities Exist for JTF Training
• Design, development, and implementation
of exercises to support JTF training are
resource intensive
• Exercises need to adapt to changes in
training audience performance and
objectives
• Requirement exists for tools to support realtime modification of exercises
Need to Improve Training Management Efficiency while Maintaining
Training Effectiveness
Large Scale Exercise Control:
Part of the Challenge
Unified Endeavor
Exercise Control
Exercise
Control
Exercise
Control
Exercise
Control
Instructor Controller
Planner/IPTL
Planner
Exercise Controller
Analyst
AAR
Cell
Personnel
Requirements
Senior Control
Scenario Management
Site Control Cells
52
Intelligence Control Cell
149
Simulation Control Center
163
OPFOR Control & Roleplayers
89
AAR Operations
Observer/Controller Team
58
Role Players/Response Cells
470
TOTAL
981
Facilitator
Response Cells
AFFOR
MARFOR
ARFOR
NAVFOR
Scenario Manager
MSEL
Cell
OPFOR
Cell
Analyst
AAR
Cell
Observers
JSOTF
Need to Reduce the Number of Personnel Required to Manage
Exercises (e.g., original JSIMS goal of 66%)
Enabling Technologies for Exercise
Management: Part of the Answer
Trainers
Instructor Agent Management
• Intelligent Agents
– To provide aid to exercise support
personnel to perform event modification (i.e.
data collection)
Instructional
Agent
Training
Planning
Agent
Exercise
Planning
Agent
Scenario
Agent
Archival
Agent
Data
Collection
Agent
SIM
C4I Layer
• Human Performance Models
– To model the behavior of exercise support
personnel tasks for conducting event
modification (controller performance
support)
• Computer-Generated Forces
– Software “hooks” to support rapidly
reconfiguring the synthetic environment
Improving real-time modification of exercises requires technology that
aids exercise support personnel and training processes
6.3 Intelligent Agents to Enhance Learning
in Large Scale M&S Exercises
Expected Payoffs:
• Reduction in the number exercise support personnel
• Enhancement in the capability to perform real-time modification of
exercises
• Reduction in the experience levels of exercise support personnel
• Improvement in the effectiveness of training exercises
• Transition of R&D products into emerging training systems
Supporting Future Naval Capabilities and Joint Desired Operational
Capabilities
Example Category 1 Training System
Requires 8 Personnel to Train 3
2 Instructor Control Stations
Scenario
Generator
3 Role Players
Scenario
Execution
Data collection
& analysis
Crewstation
Displays and
Controls
3 Observers
3 Trainees
6.3 Synthetic
Cognition
Operational
Vision
Training System
w/ for
Simulated
Forces
Requires
1-3 Trainees
Team1 Instructor
Training for
(SCOTT)
1 2 Instructor Control Stations
3 Role Players
JointSAF
Synthetic Battlespace
w/ improved HBMs
Scenario
Generator
Automated Training
Management w/
Instructional Agents
Scenario
Execution
Data collection
& analysis
Crewstation
Displays and
Controls
3 Observers Expert Models
for Intelligent Tutoring
1 3 Trainees
2 Simulated Teammates
6.3 Synthetic Cognition for Operational
Team Training (SCOTT)
Scenario
Generator
Scenario
Execution
Data
collection
& analysis
E-2C NFO
OBJECTIVES
Prototype E-2C Intelligent Tutoring System
for Training Advanced Aviation Team Skills in
Deployed Environments:
• Automated Performance Measurement
• Intelligent Software for Diagnosing
Performance Errors
• On-Line Feedback
• Post-Mission Debriefing
• Robust Speech Interface
APPROACH
Apply Advanced Cognitive Modeling
Techniques for:
• Synthetic Teammates
• Intelligent Adversaries
• Instructional Agents to automate :
–Objective based scenario generation
–MOE/MOP data collection
–diagnosis
–on-line feedback
PAYOFF
•
•
•
•
•
•
•
Reduce Time to Mastery by 30%
Increase Mission Effectiveness by 25%
Reduce Aviation Mishaps by 10%
Enable Training Just-In-Time, On-Demand,
Anywhere
Incorporate Emerging Intelligent Training
Features
Reducing Required Instructors by 50%
Provide Specifications for F/18 PTT
FY01 Synthetic Cognition for Virtual Environment
Landing Cushion Air Craft (VELCAC)
synthetic Navigator
JSAF
Objectives
Develop computer-generated synthetic Navigator
• Interacts with human-in-the-loop operator(s)
• Provides speech communications with Craftmaster
HLA
• Interfaces with VELCAC
Network
• Makes decision based on tactical and environmental
conditioning cue
VELCAC
Integrate VELCAC into JSAF battlespace environment
Transition current work efforts to VIRTE Demo I
Approach
 Perform knowledge engineering on Navigator position
 Develop the cognitive architecture
 Model the Navigator crew position
Payoff
Reduce manning
• Ability to training Craftmaster without live Navigator
present
• Increase availability of training
Develop API/ communication shell between Navigator
model and VELCAC
Interoperability with other simulation platforms
Integrate synthetic model into VELCAC
Transition existing work to support VIRTE initiative
 Populate additional entities using JSAF
 indicates initial accomplishments
Integrated CGF programs for Naval Distributed Team Training
6.1 Situation Awareness Panel for
JointSAF (TACAIRSOAR) entities
6.1 Investigation of SOAR Improvements
6.2 FITER- cognitive & behavioral
principles for distributed team training
6.2 CAATS-delivers Model
Based Tutoring Strategies
TACAIRSOAR in JointSAF
PMA-205 Deployable E-2C Trainer
PMA-205 Air Warfare Training
FA-18 Pilot
F/18 Part
Task Trainer
E-2C NFO
HLA
Network
Joint Synthetic Battlespace
6.4 Improved F-18 Automated Wingman
6.4 Deployed Aviation Training
PMS-430 Battle Force Tactical Trainer
MC AAAV & LCAC
VELCAC
Anti-Air Warfare
6.3 SCOTTTraining
w/Synthetic
& Virtual
entities with
Intelligent
Tutoring
MC MOUT
6.2 Composable Behaviors in JointSAF
6.1 Model of Naturalistic
Decision Making
6.2 SYNTHERS - Training with CGF Teammates
6.1 Diagnostic Utility of Math
Modeling of Cognition
6.1 Soft Computing Techniques
within Cognitive Architectures