Naval Program on Human Modeling for Computer Generated Forces

1. 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

2. 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

3. 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

4. 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

5. 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 fullyautomated 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

6. 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

7. An Integrated Research Approach 6.4+ Apply HBR and CGFs to deployable Navy & MC Training Simulations and define specifications for implementing in future platforms 6.3 Demonstrate and measure the effectiveness of HBR and CGFs in prototype Navy & MC Training Simulations 6.2 Investigate the feasibility of instructional strategies using HBR and CGFs 6.1 HBR and CGF architecture development and studies Products transition forward Requirements and research questions flow back Defense Technology Objective (DTO) HS.30 Realistic Cognitive and Behavioral Representations in Simulation

8. CGF R&D Programs & Transitions 6.1/SBIR 6.2 6.3 6.4 Acquisition + ONR M&S Realistic Human Modeling • Computer Generated Forces • PE0602233N • Teammates • JSAF • Tutoring • Synthetic Cognition for Operational Team Training (SCOTT) • PE0603707N • E-2C • LCAC • Air Warfare Training Development Research Tasks • Deployed Trng Technology Eval • Deployed Trng Reqmts Analysis • Deployed Aviation Team Trng • Intelligent Synthetic Forces Deployable Tactical Aviation Trng Sys (DTATS) BFTT, SWOS Diagnostic Utility of Math Modeling Support ACTC: NSAWC, Weap Schools, Fleet Sqdns, Air Wing Trng Fleet Integration Training Evaluation Research (FITER) PE0602233N • Transportable Strike Assault Rehearsal System (TSTARS) • PE0603707N • F/18 Distributed Team Training for Multi-Platform Aviation Missions SBIR Phase II FA-18 (17C-OFP) PTT AAAV, JSF, DD21, LPD-17 CVNX, & other new construction Dynamic Assessment PE0602233N • Intelligent Agents to Enhance Learning in Large Scale Exercises • PE0603707N • JSIMS JSIMS, ONESAF DMT, MCASMP Advanced Embedded Training (ATD)

9. 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)

10. 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)

11. Two 6.3 Programs….. Targeting Both Ends of the Continuum Category 3 Joint Task Forces Exercises Category 1 Individual Training • 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

12. 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 real-time modification of exercises Need to Improve Training Management Efficiency while Maintaining Training Effectiveness

13. Unified Endeavor Exercise Control Senior Control Scenario Management Site Control Cells Intelligence Control Cell Simulation Control Center OPFOR Control & Roleplayers AAR Operations Observer/Controller Team Role Players/Response Cells TOTAL Personnel Requirements 52 149 163 89 58 470 981 Exercise Control Exercise Control Exercise Control Exercise Control Exercise Control Exercise Control IAGENTS Instructor Controller Exercise Controller Planner/IPTL Planner Analyst Analyst AAR Cell Facilitator Facilitator Response Cells Response Cells Scenario Manager Scenario Manager Analyst AAR Cell MSEL OPFOR AFFOR AFFOR MARFOR MARFOR Observers Cell Cell ARFOR ARFOR NAVFOR NAVFOR JSOTF JSOTF Large Scale Exercise Control: Part of the Challenge Need to Reduce the Number of Personnel Required to Manage Exercises (e.g., original JSIMS goal of 66%)

14. Enabling Technologies for Exercise Management: Part of the Answer Trainers Instructor Agent Management Instructional Agent Archival Agent Training Planning Agent Exercise Planning Agent Data Collection Agent Scenario Agent SIM C4I Layer • 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 Improving real-time modification of exercises requires technology that aids exercise support personnel and training processes

15. 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

16. Example Category 1 Training SystemRequires 8 Personnel to Train 3 3 Role Players 2 Instructor Control Stations Scenario Generator Scenario Execution Data collection & analysis Crewstation Displays and Controls 3 Observers 3 Trainees

17. 6.3 Synthetic Cognition for Operational Team Training (SCOTT) Vision Training System w/ Simulated ForcesRequires 1 Instructor for 1-3 Trainees 3 Role Players 2 Instructor Control Stations 1 JointSAF Synthetic Battlespace w/ improved HBMs Scenario Generator Scenario Execution Data collection & analysis Crewstation Displays and Controls Automated Training Management w/ Instructional Agents 3 Expert Models for Intelligent Tutoring 1 2 Simulated Teammates 3 Observers 3 Trainees

18. 6.3 Synthetic Cognition for Operational Team Training (SCOTT) Scenario Generator Scenario Execution 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 Data collection & analysis 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

19. FY01 Synthetic Cognition for Virtual Environment Landing Cushion Air Craft (VELCAC) 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 synthetic Navigator JSAF HLA Network VELCAC 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 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

20. 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 CAATS-delivers Model Based Tutoring Strategies 6.2 FITER- cognitive & behavioral principles for distributed team training PMA-205 Deployable E-2C Trainer PMA-205 Air Warfare Training E-2C NFO HLA Network TACAIRSOAR in JointSAF FA-18 Pilot F/18 Part Task Trainer Joint Synthetic Battlespace 6.4 Improved F-18 Automated Wingman 6.4 Deployed Aviation Training MC AAAV & LCAC VELCAC PMS-430 Battle Force Tactical Trainer Anti-Air Warfare MC MOUT 6.1 Diagnostic Utility of Math Modeling of Cognition 6.2 Composable Behaviors in JointSAF 6.1 Soft Computing Techniques within Cognitive Architectures 6.1 Model of Naturalistic Decision Making 6.2 SYNTHERS - Training with CGF Teammates 6.3 SCOTT- Training w/Synthetic & Virtual entities with Intelligent Tutoring