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Naval Program on Human Modeling for Computer Generated Forces. Denise Lyons, Ph.D. NAWCTSD, Air 4962 [email protected]. Harold Hawkins, Ph.D. Office of Naval Research [email protected]. Fleet Requirements Identified. - PowerPoint PPT Presentation
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Naval Program onHuman Modeling for
Computer Generated Forces
Denise Lyons, Ph.D.NAWCTSD, Air [email protected]
Harold Hawkins, Ph.D.Office of Naval Research
• 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
Fleet Requirements Identified
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
• 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.
CGFs for Military Simulations:Automated Forces vs. Semi-Automated Forces
• 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
Our Research Identified Required Enabling Technologies: • Human Behavior Modeling • Intelligent Agents• Computer Generated Forces
An Integrated Research Approach
6.1HBR and CGF architecture development and studies
6.2Investigate the
feasibility of instructional
strategies using HBR and CGFs
6.3Demonstrate 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.30Realistic Cognitive and Behavioral Representations in Simulation
Defense Technology Objective (DTO) HS.30Realistic Cognitive and Behavioral Representations in Simulation
CGF R&D Programs & Transitions
6.2 6.46.3
Fleet Integration Training
Evaluation Research (FITER) PE0602233N
Computer Generated
ForcesPE0602233N
• Teammates• JSAF
• Tutoring
Dynamic Assessment
PE0602233N
Synthetic Cognition for Operational
Team Training (SCOTT)PE0603707N
• E-2C• LCAC
Transportable Strike Assault
Rehearsal System
(TSTARS)PE0603707N
• F/18
Deployable Tactical Aviation Trng Sys
(DTATS)
Support ACTC:NSAWC, Weap
Schools, Fleet Sqdns, Air Wing Trng
AAAV, JSF, DD21, LPD-17 CVNX, & other new construction
Air Warfare Training
Development Research Tasks
• Deployed Trng Technology Eval
• Deployed Trng Reqmts Analysis
• Deployed Aviation Team Trng
• Intelligent Synthetic Forces
6.1/SBIR
BFTT, SWOS
Distributed Team
Training for Multi-
Platform Aviation Missions
SBIR Phase II
Acquisition +
Diagnostic Utility of
Math Modeling
FA-18 (17C-OFP) PTT
ONR M&S Realistic Human
Modeling
Intelligent Agents to Enhance
Learning in Large Scale ExercisesPE0603707N
• JSIMS
Advanced Embedded
Training (ATD)
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: What is required to carry out the task?
• 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 3Joint Task Forces Exercises
6.3 Intelligent Agents to Enhance Learning in Large Scale Exercises
• Targeted for JSIMS
6.3 Synthetic Cognition for Operational Team Training (SCOTT)
• Deployed/Embedded training• E2-C• VELCAC
Category 1Individual Training
• 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 real-time modification of exercises
Need to Improve Training Management Efficiency while Maintaining Need to Improve Training Management Efficiency while Maintaining Training EffectivenessTraining Effectiveness
Meeting Important Operational Requirements:
6.3 Intelligent Agents to Enhance Learning in Large Scale M&S Exercises
Response Cells
AFFOR
ARFOR
JSOTF
MARFOR
NAVFOR
Scenario Manager
Instructor Controller
Exercise Control Exercise Control Exercise Control Exercise Control Exercise Control
Planner Planner/IPTL
Response Cells
AFFOR
ARFOR
JSOTF
MARFOR
NAVFOR Cell
MSEL
Exercise Control
Exercise Controller Analyst
AAR Cell
Facilitator
Analyst AAR Cell
Observers
Facilitator
Analyst
OPFOR
Scenario Manager
Cell
Unified EndeavorExercise Control
Senior ControlScenario ManagementSite Control Cells
Intelligence Control Cell
Simulation Control Center
OPFOR Control & Roleplayers
AAR OperationsObserver/Controller Team
Role Players/Response Cells
TOTAL
PersonnelRequirements
52
149
163
89
58
470
981
Large Scale Exercise Control:Part of the Challenge
Need to Reduce the Number of Personnel Required to Manage Need to Reduce the Number of Personnel Required to Manage Exercises (e.g., original JSIMS goal of 66%)Exercises (e.g., original JSIMS goal of 66%)
• Intelligent Agents– To provide aid to exercise support
personnel to perform event modification (i.e. data collection)
• 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
Enabling Technologies for Exercise Management: Part of the Answer
Improving real-time modification of exercises requires technology that Improving real-time modification of exercises requires technology that aids exercise support personnel and training processesaids exercise support personnel and training processes
Trainers
C4I Layer
SIM
Instructor Agent Management
InstructionalAgent
ArchivalAgent
TrainingPlanning
Agent
ExercisePlanning
Agent
ScenarioAgent
DataCollection
Agent
Expected Payoffs:
Supporting Future Naval Capabilities and Joint Desired Operational Supporting Future Naval Capabilities and Joint Desired Operational CapabilitiesCapabilities
• 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
6.3 Intelligent Agents to Enhance Learning in Large Scale M&S Exercises
3 Role Players
Scenario Generator
ScenarioExecution
Data collection& analysis
CrewstationDisplays and
Controls
2 Instructor Control Stations
3 Trainees
3 Observers
Example Category 1 Training SystemRequires 8 Personnel to Train 3
3 Role Players
Vision Training System w/ Simulated ForcesRequires 1 Instructor for 1-3 Trainees
Scenario Generator
ScenarioExecution
Data collection& analysis
CrewstationDisplays and
Controls
2 Instructor Control Stations
3 Trainees
3 Observers
2 Simulated Teammates1
JointSAFSynthetic Battlespace
w/ improved HBMs
Expert Models for Intelligent Tutoring
3
6.3 Synthetic Cognition for Operational Team Training (SCOTT)
1
Automated Training Management w/
Instructional Agents
OBJECTIVESPrototype 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
APPROACHApply 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
ScenarioExecution
Data collection& analysis
E-2C NFO
6.3 Synthetic Cognition for Operational Team Training (SCOTT)
Scenario Generator
FY01 Synthetic Cognition for Virtual Environment Landing Cushion Air Craft (VELCAC)
VELCAC
JSAF
HLA
Network
synthetic Navigator Objectives
Develop computer-generated synthetic Navigator
• Interacts with human-in-the-loop operator(s)
• Provides speech communications with Craftmaster
• Interfaces with VELCAC
• Makes decision based on tactical and environmental conditioning cue
Integrate VELCAC into JSAF battlespace environment
Transition current work efforts to VIRTE Demo I
Payoff
Reduce manning
• Ability to training Craftmaster without live Navigator present
• Increase availability of training
Interoperability with other simulation platforms
Transition existing work to support VIRTE initiative
Approach Perform knowledge engineering on Navigator position
Develop the cognitive architecture
Model the Navigator crew position
Develop API/ communication shell between Navigator model and VELCAC
Integrate synthetic model into VELCAC
Populate additional entities using JSAF
indicates initial accomplishments
Integrated CGF programs for Naval Distributed Team Training
6.2 Composable Behaviors in JointSAF
6.2 SYNTHERS - Training with CGF Teammates
6.2 CAATS-delivers Model Based Tutoring Strategies6.2 FITER- cognitive & behavioral
principles for distributed team training
6.3 SCOTT-Training
w/Synthetic & Virtual
entities with Intelligent Tutoring
HLA NetworkF/18 Part
Task Trainer
PMA-205 Air Warfare Training
FA-18 Pilot
PMA-205 Deployable E-2C TrainerE-2C NFO
PMS-430 Battle Force Tactical Trainer
Anti-Air WarfareMC AAAV & LCAC
VELCAC
TACAIRSOAR in JointSAF
Joint Synthetic Battlespace
6.4 Improved F-18 Automated Wingman 6.4 Deployed Aviation Training
MC MOUT
6.1 Model of Naturalistic Decision Making
6.1 Soft Computing Techniqueswithin Cognitive Architectures
6.1 Situation Awareness Panel for JointSAF (TACAIRSOAR) entities
6.1 Diagnostic Utility of Math Modeling of Cognition
6.1 Investigation of SOAR Improvements