Research on Development of Intelligent Tutoring Systems ITS to Support Embedded Training ET in Future Army Systems pr

1. Research on Development of Intelligent Tutoring Systems (ITS)to Support Embedded Training (ET)in Future Army Systemspresented by: Henry MarshallRDECOM Simulation and TrainingTechnology Center (STTC) 

2. 2 Outline Background in ET Proposed ITS Design/ Intelligent Structured Training Concept Applications C2V Robotics Testbed CAT ATD Testbed Experiment Virtual Warrior Testbed Issues and Conclusion

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4. 4 ITS and ET, a Good Match? Training (to include Embedded Training) is a Key Performance Parameter for the Future Combat System (FCS) and Ground Soldier Systems (GSS). Also requirement for Abrams, Bradley and Stryker. ET intent is to fully embed training system on the operational platforms The instructional staff at current Army fixed sites will likely not be available for deployed forces Can ITS-based technology be integrated with current simulation common components to replace the role of instructors for ET?

5. 5 FCS/ET � Training Challenge New paradigm requires scenario-based practice for FCS warfighters CCTT has successfully used a Structured Training concept for Basic Techniques, Task and Procedures Structured Training for CCTT STRUCTT Formal tactical doctrine for FCS operational concept is still evolving Desirable to minimize costs of developing and administering training � reduce requirements for human instructors and simplify scenario definition ITS are effective for simulating some of the benefits of a human instructor, especially for a domain with focused, task-based exercises Enter our ITS research to prototype possible solutions

6. 6 Intelligent Structured Training? Goals are to Maximize Simulation Common Components in developing ITS system Operate in typical virtual training environment Develop a system capability of replacing instructors where possible Assumption Because of the complexity of free play exercises and the number of possible solutions, ITS would be useable for only a limited set of predefined training scenarios Answer Develop an ITS-based system that supports finite state transitions and provides prompts and feedback, operating in a virtual training scenario. We have named this Intelligent Structured Training (IST)

7. 7 What are they? Transition networks executing in coordination with a simulation to gather data about instructionally significant events and states, and make evaluation conclusions in real time Why use them in an ITS? Several benefits: Modularity � they can be used separately or in conjunction for a variety of scenarios Instructional correspondence � individual instructional principles can be associated with independent evaluations Integration � the FSM structure is easily integrated with free-play simulations and maps well to diagnostics for widely varied outcomes Authoring ease � they can be represented visually, making them easy for non-programmers to create, maintain, and revise Application implements as a Behavior Transition Network (BTN)

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9. 9 Intelligent Structured Training Concept Goal: Provide the benefits of instructor-led training, in an embedded setting Methods Based on Intelligent Tutoring System (ITS) technology Intelligent agents perform automated evaluation during execution Subject matter experts define agent behaviors Behaviors defined in hierarchical behavior transition networks (BTN) Real-time feedback, hinting, or coaching presented in Soldier Machine Interface Must operate on small �footprint� of embedded computer systems

10. 10 C2V Experiment Credit LTC Mike Sanders FA 57, mike.sanders@us.army.mil

11. 11 Command and Control Vehicle Crewstation Based on CAT ATD The device used in this experiment was the Robotics� NCO crew station in a prototype FCS C2V. The Robotics� NCO crew station consists of six, flat panel touch screens for the visualization and control of entities within the synthetic training environment (STE), haptic devices for the control of robotics assets (driver/gunners yokes, buttons, triggers, etc) speakers for audio output, a finite state machine (FSM) embedded simulation component for computer generated forces (CGF) behaviors, a tutoring-based feedback application system, and a data collection system. The device used in this experiment was the Robotics� NCO crew station in a prototype FCS C2V. The Robotics� NCO crew station consists of six, flat panel touch screens for the visualization and control of entities within the synthetic training environment (STE), haptic devices for the control of robotics assets (driver/gunners yokes, buttons, triggers, etc) speakers for audio output, a finite state machine (FSM) embedded simulation component for computer generated forces (CGF) behaviors, a tutoring-based feedback application system, and a data collection system.

12. 12 Command and Control Vehicle Crewstation The top three flat panel screens display a three-dimensional view of the STE, each representing a different view from the robotic vehicles. The top left screen displays the view from the visual sensor mounted on the Unmanned Aerial Vehicle (UAV). The top center screen displays the driver�s position view from the visual sensor mounted on the Unmanned Ground Vehicle (UGV). The top right screen displays the view seen by the visual sensor from the gunner�s position in the turret. When displayed, feedback prompts also appear on this screen. The top three flat panel screens display a three-dimensional view of the STE, each representing a different view from the robotic vehicles. The top left screen displays the view from the visual sensor mounted on the Unmanned Aerial Vehicle (UAV). The top center screen displays the driver�s position view from the visual sensor mounted on the Unmanned Ground Vehicle (UGV). The top right screen displays the view seen by the visual sensor from the gunner�s position in the turret. When displayed, feedback prompts also appear on this screen.

13. 13 Command and Control Vehicle Crewstation The primary control environment that the test subjects used is called an Operator Control Unit (OCU) and is displayed on the bottom center screen. The OCU functions as the control interface for networked robotic vehicles under the test subject's command. The OCU operates directly with the OneSAF Testbed Baseline (OTB) to control and monitor status for robotic entities under the test subject�s control. It also provides situational awareness to the test subject, providing a map, scenario defined graphic control measures (GCM), and icon tracking & locations. An additional user interface, called the Robotic Assets/Mission Status Tool provides a high level control user interface both for seeing the status of the unmanned vehicles under the control of the operator, for initiating commands to the vehicle entities, submitting reports, and acknowledging targets. It is located on the lower left screen, and the test subjects touch the screen to activate the appropriate buttons. The lower right screen is yet another user interface, called the Tele-Operation Asset Tool and it provides a high level control user interface both for seeing the status of the Tele-Op UGV vehicle under the control of the operator and issuing commands.The primary control environment that the test subjects used is called an Operator Control Unit (OCU) and is displayed on the bottom center screen. The OCU functions as the control interface for networked robotic vehicles under the test subject's command. The OCU operates directly with the OneSAF Testbed Baseline (OTB) to control and monitor status for robotic entities under the test subject�s control. It also provides situational awareness to the test subject, providing a map, scenario defined graphic control measures (GCM), and icon tracking & locations. An additional user interface, called the Robotic Assets/Mission Status Tool provides a high level control user interface both for seeing the status of the unmanned vehicles under the control of the operator, for initiating commands to the vehicle entities, submitting reports, and acknowledging targets. It is located on the lower left screen, and the test subjects touch the screen to activate the appropriate buttons. The lower right screen is yet another user interface, called the Tele-Operation Asset Tool and it provides a high level control user interface both for seeing the status of the Tele-Op UGV vehicle under the control of the operator and issuing commands.

14. 14 ITS Generated Feedback Prompt

15. 15 Approach and Scenario Overview Task Analysis for FCS equipped Unit of Action (UA) Required Functional Capabilities include sensor fusion and engagement techniques User ~ Robotics Operator in the C2V at the Company level Scenario ~ Route Reconnaissance ITS modified to trigger OneSAF behaviors via DIS Task decomposition focused on the common functional capabilities of the FCS C2V, unmanned robotic vehicles and fires systems organic to either the Infantry or Mounted Combat System (MCS) Company levels. Additional fires platforms organic to the CA Bn were included in this functional capabilities review, as well as aviation assets, although aviation assets were quickly eliminated to reduce the scope of the evaluation set. Information dominance is a cornerstone of the Future Force, so sensor fusion became the focus of the task analysis. The O&O defines fusion as �the combining or blending of data and information from single or multiple sources into information.� This fusion provides the FCS-equipped UA the �Quality of Firsts� which enables it to dominate the battlefield. This battlefield dominance centers on the coupling of new lethality concepts (conceptual engagement types) and methods of receiving targeting information. Lethality modes include the traditional Line of Sight (LOS) and Beyond Line of Sight (BLOS) engagements (direct fire) and Non-Line of Sight (NLOS) (indirect fire). BLOS is an extension of the traditional direct fire engagement because the shooter �sees� the target through a sensor that has a sensor-to-shooter link. Cooperative Engagement is an engagement method where the sensor and shooter are not together in a single platform. Point and Shoot is a subset of Cooperative Engagement and allows a platform to designate a target for engagement by a different platform. Point and Shoot requires highly responsive effect (5 seconds or less) but occurs within the same tactical echelon. Task decomposition focused on the common functional capabilities of the FCS C2V, unmanned robotic vehicles and fires systems organic to either the Infantry or Mounted Combat System (MCS) Company levels. Additional fires platforms organic to the CA Bn were included in this functional capabilities review, as well as aviation assets, although aviation assets were quickly eliminated to reduce the scope of the evaluation set. Information dominance is a cornerstone of the Future Force, so sensor fusion became the focus of the task analysis. The O&O defines fusion as �the combining or blending of data and information from single or multiple sources into information.� This fusion provides the FCS-equipped UA the �Quality of Firsts� which enables it to dominate the battlefield. This battlefield dominance centers on the coupling of new lethality concepts (conceptual engagement types) and methods of receiving targeting information. Lethality modes include the traditional Line of Sight (LOS) and Beyond Line of Sight (BLOS) engagements (direct fire) and Non-Line of Sight (NLOS) (indirect fire). BLOS is an extension of the traditional direct fire engagement because the shooter �sees� the target through a sensor that has a sensor-to-shooter link. Cooperative Engagement is an engagement method where the sensor and shooter are not together in a single platform. Point and Shoot is a subset of Cooperative Engagement and allows a platform to designate a target for engagement by a different platform. Point and Shoot requires highly responsive effect (5 seconds or less) but occurs within the same tactical echelon.

16. 16 Robotics Operator Tasks Coordinated use of robotic assets Example: Maintain proper separation between air asset and ground vehicle Proper reporting procedures Example: Send SITREP after reaching a control measure Proper engagement procedures Example: Lase a target before sending call for fire Proper use of asset control tools Example: Make sure a vehicle is currently being controlled before issuing commands in the control interface.

17. 17 Experimental Design Test group: 20 subjects Comparison conditions Immediate Directive Feedback (IDF) only ITS generated feedback through prompts AAR Only (Delayed Feedback) Human facilitated AARs used open-ended, content neutral prompts Experiment phases Training and test phase Initial human-tutored and computer-aided instruction Two-phased execution Paper and pencil test Retention and Post-test phase after 1 week delay ITS generated feedback: If the trainees response was incorrect, immediate feedback is provided in the form of an error message provided to the test subject. No immediate feedback was provided for following correct procedures. The three types of immediate feedback included: battlefield heuristic feedback, �Conduct a sensor scan before beginning movement�; error detection feedback, �Submit a report anytime there is a change to the tactical situation�; directive feedback, �You have failed to correctly submit a SITREP. The correct procedure is . . .�. Directive Feedback prompts were triggered either when a test subject failed to take an appropriate action, after receiving an error detection prompt, or conducted a procedure incorrectly.ITS generated feedback: If the trainees response was incorrect, immediate feedback is provided in the form of an error message provided to the test subject. No immediate feedback was provided for following correct procedures. The three types of immediate feedback included: battlefield heuristic feedback, �Conduct a sensor scan before beginning movement�; error detection feedback, �Submit a report anytime there is a change to the tactical situation�; directive feedback, �You have failed to correctly submit a SITREP. The correct procedure is . . .�. Directive Feedback prompts were triggered either when a test subject failed to take an appropriate action, after receiving an error detection prompt, or conducted a procedure incorrectly.

18. 18 Experimental Results Procedural Errors note An ANOVA was preformed with the between subjects factor being Feedback Condition, F (1,18) = 5.87 and Training Trials, F (2,24) = 13.05 with a p<.05. The ANOVA results tells us that there are significant differences in the number of IDF prompts triggered by each test subject and that these differences are explained by both the number of training trials (learning occurring over time) and by the type of Feedback received. The significantly lower number of error prompts triggered during the execution of training and transfer scenarios demonstrates that the IDF Only feedback condition had a significant effect on the acquisition and transfer of the performance procedures. The differences in retention scores following feedback while large was not statistically significant.Procedural Errors note An ANOVA was preformed with the between subjects factor being Feedback Condition, F (1,18) = 5.87 and Training Trials, F (2,24) = 13.05 with a p<.05. The ANOVA results tells us that there are significant differences in the number of IDF prompts triggered by each test subject and that these differences are explained by both the number of training trials (learning occurring over time) and by the type of Feedback received. The significantly lower number of error prompts triggered during the execution of training and transfer scenarios demonstrates that the IDF Only feedback condition had a significant effect on the acquisition and transfer of the performance procedures. The differences in retention scores following feedback while large was not statistically significant.

19. 19 Analysis The timing and type of feedback received during training does affect the acquisition, retention and transfer of knowledge Procedural knowledge Retention scores elevated in both comparison conditions Lower number of errors with Immediate Directive Feedback Conceptual knowledge Retention scores elevated in both comparison conditions Higher retention scores with AAR Bottom line � proven learning from embedded ITS feedback! This could provide deployable training and save $$$ if authoring cost were economical Improved Pre-Brief or Postbreif capability could help improve the ITS Conceptual Knowledge retention

20. 20 TARDEC CAT ATD Integration

21. 21 Virtual Warrior Experiment Credit Major Jason Sims FA 57, jason.sims@us.army.mil

22. 22 Authoring Tool Design

23. 23 VW-ITS Design

24. 24 VW-ITS Issues Numerous problems with OTB SAF behaviors filling out the rest of the Squad and OPFOR Evaluations limited by troop availability Most prefer system that would allow most of the squad to be live (e.g. team training) as opposed to interacting with SAF Virtual Locomotion of VW not liked, e.g. ability to move around database. Exposure to fire also a concern. Difficultly sending messages w/ C2 system

25. 25 Issues Integration/Interoperation with OOS and Training Common Components Best Future Direction, Game SDK vs. Training Common Components? Mix with a Operational Coach Mentor? Experiment with Enhanced / Muti-Modal feedback for ITS Integration into vehicles information/ET systems Transition with focus to PM - systems we developed were focused on experimentation Authoring systems for ease of production and usability by topic SMEs. Low Software License Costs. Production costs per tasks will drive economics of ITS acceptance. Explore Team Training Improved CGF for ITS for control of OPFOR/BLUFOR behaviors Exercise automated Pre-Brief/Post Brief Ability to adjust difficultly level either before execution or dynamically

26. 26 Discussion??