Naval Program onHuman Modeling for

Computer Generated Forces

Denise Lyons, Ph.D.NAWCTSD, Air 4962LyonsDM@navair.navy.mil

Harold Hawkins, Ph.D.Office of Naval Research

HawkinH@onr.navy.mil

• 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