1. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Army Digitization Research Initiative Dr. Richard A. Volz (Computer Science) Dr. Tom Ioerger (Computer Science) Dr. John Yen (Computer Science) Dr. James Wall (TCAT) Randy Elms (TCAT) Look College of Engineering Town Hall Meeting May 11, 2000 Dr. Richard A. Volz (Computer Science) Dr. Tom Ioerger (Computer Science) Dr. John Yen (Computer Science) Dr. James Wall (TCAT) Randy Elms (TCAT) Look College of Engineering Town Hall Meeting May 11, 2000

2. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN  UT Austin & Texas A&M proposed a joint 5 year congressional initiative to support digitization research at Fort Hood  Congressionally funded, equally split between UT & A&M  Army Digitization Office (ADO) solicited proposals from Army Major Commands  Major Commands and Agencies Participating:  National Simulation Center  OneSAF TRADOC Program Office  Simulation, Training and Instrumentation Command  Central Technical Support Facility  Full-time, on-site presence at Fort Hood  UT Austin & Texas A&M proposed a joint 5 year congressional initiative to support digitization research at Fort Hood  Congressionally funded, equally split between UT & A&M  Army Digitization Office (ADO) solicited proposals from Army Major Commands  Major Commands and Agencies Participating:  National Simulation Center  OneSAF TRADOC Program Office  Simulation, Training and Instrumentation Command  Central Technical Support Facility  Full-time, on-site presence at Fort Hood Background

3. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Tasks & Funding 1 - Conduct Battle Staff Training FEA 2 - Correlate to Mission Essential Tasks 3 - Model a Set of Staff Behaviors 4 - Conduct C4I - Simulation Interface Analysis 1 - Embedded Training Design Support 2 - Joint Mapping Tool Kit 3D Enhancement 3 - Simulation C4I - SIMCI Support 4 - One SAF/Staff Training Project 5 - Training & Operational Data Synchronizer FY 01 ? Tasks Tasks $2M [Requested] $1M [Army budgeted] Funds $2M [Congress set aside] $2M [Congress] $.5M [Army contribution] Funds FY 00 FY 99

4. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Objective ä To develop an agent-based computational framework for simulating adaptive TOC (Tactical Operating Center) teamwork process for reducing the cost of training digital force ä Generate more intelligent/autonomous behavior at aggregate level (companies, battalions, brigades) for networked “wargame” simulations ä To develop an agent-based computational framework for simulating adaptive TOC (Tactical Operating Center) teamwork process for reducing the cost of training digital force ä Generate more intelligent/autonomous behavior at aggregate level (companies, battalions, brigades) for networked “wargame” simulations

5. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN What is agent-based adaptive TOC Teamwork ? ä Virtual TOC staff (I.e., software) interacts with the human trainees and adapts their actions to ä battlefield situations (e.g., enemy maneuver & intent) ä battle plan, commander’s intent ä trainee’s actions ä trainee’s profiles ä training objectives ä Virtual TOC staff (I.e., software) interacts with the human trainees and adapts their actions to ä battlefield situations (e.g., enemy maneuver & intent) ä battle plan, commander’s intent ä trainee’s actions ä trainee’s profiles ä training objectives

6. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Major Technical Barriers ä Needs a language for describing TOC teamwork processes, strategies, and procedures. ä Needs an agent algorithm that can react to dynamic changes in the environment. ä Need to simulate cooperative interactions among friendly units (e.g. information sharing, coordination). ä Needs a language for describing TOC teamwork processes, strategies, and procedures. ä Needs an agent algorithm that can react to dynamic changes in the environment. ä Need to simulate cooperative interactions among friendly units (e.g. information sharing, coordination).

7. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Our Approach ä Developed a Task Representation Language (TRL) suitable for describing TOC teamwork processes and actions. ä Developed a integrated reactive planning and plan execution monitoring algorithm for simulating adaptive TOC teamwork. ä Developed a Task Representation Language (TRL) suitable for describing TOC teamwork processes and actions. ä Developed a integrated reactive planning and plan execution monitoring algorithm for simulating adaptive TOC teamwork.

8. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Task Representation Language (TRL) ä A hierarchical task-decomposition language based on AI planning systems ä Separates tasks (what to do) from methods (how to do it) ä Captures staff procedures ä Decision points based on queries to a knowledge base (JESS) ä A hierarchical task-decomposition language based on AI planning systems ä Separates tasks (what to do) from methods (how to do it) ä Captures staff procedures ä Decision points based on queries to a knowledge base (JESS)

9. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN The Software Architecture Bn TOC Behavioral Knowledge (TRL) Initial Battlefield Situation (JESS) TRL Parser Load Knowledge Base Generic Tasks, Methods, and Procedures Instantiated Tasks, Methods, and Procedures Reactive Planning & Adaptive Execution Brigade Interface Puckster Interface Bn TOC World Model (JESS) OTB-Agent Interface OTB puckster Brigade Staff

10. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Adaptive Plan Execution ä Detects changes in the environment using JESS. ä Reacts to the change by invoking the reactive planner. ä Actions include sending reports to Brigade and puckster ä Detects changes in the environment using JESS. ä Reacts to the change by invoking the reactive planner. ä Actions include sending reports to Brigade and puckster Instantiated Tasks, Methods, and Procedures Adaptive Plan Execution Brigade Interface Puckster Interface Bn TOC World Model (JESS) Reactive Planner

11. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Prototype Demo Architecture

12. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Other Year 1 Accomplishments  Encoded several Battalion tasks for a “Movement to Combat” scenario  Developed an TRL parser to automate the translation from TRL knowledge to Java.  Developed an agent interface to OTB for automating updates to the agent’s ``world model’’.  Successfully implemented a distributed prototype system using Java and RMI.  Encoded several Battalion tasks for a “Movement to Combat” scenario  Developed an TRL parser to automate the translation from TRL knowledge to Java.  Developed an agent interface to OTB for automating updates to the agent’s ``world model’’.  Successfully implemented a distributed prototype system using Java and RMI.

13. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Additional Funding Obtained ä Budget for Year 2 is increased by 25% to $ 2.5 million. ä Succeeded in obtaining a DARPA MURI grant on theories and technologies for agent-based team/group training for the Air Force ($4 million, 3 yr + 2 optional yr) ä Budget for Year 2 is increased by 25% to $ 2.5 million. ä Succeeded in obtaining a DARPA MURI grant on theories and technologies for agent-based team/group training for the Air Force ($4 million, 3 yr + 2 optional yr)

14. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Lessons Learned ä Justify the use of new technology ä Agent-based digital force trainer can reduce training cost and improve the ``rigor’’ of training. ä Educate the sponsor and the customer about the technology used. ä Part of the first IPR is a brief tutorial on intelligent agents. ä Balance innovation and practicality ä Keep asking us “Why this can not be done by a government contractor?” and “Will it be ready for the demo?” ä Justify the use of new technology ä Agent-based digital force trainer can reduce training cost and improve the ``rigor’’ of training. ä Educate the sponsor and the customer about the technology used. ä Part of the first IPR is a brief tutorial on intelligent agents. ä Balance innovation and practicality ä Keep asking us “Why this can not be done by a government contractor?” and “Will it be ready for the demo?”

15. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN Lessons Learned ä Manage their expectations, yet keep them excited. ä Prepare to answer “Why can’t you do X?” ä Identify champions and recruit supporters ä especially from Aggies, with persistence ä Understand each stake holder’s self interests ä communicate and collaborate with UT, yet protect our interests. ä Teamwork is critical, but difficult ä Manage their expectations, yet keep them excited. ä Prepare to answer “Why can’t you do X?” ä Identify champions and recruit supporters ä especially from Aggies, with persistence ä Understand each stake holder’s self interests ä communicate and collaborate with UT, yet protect our interests. ä Teamwork is critical, but difficult

16. TEXAS A&M UNIVERSITY AND THE UNIVERSITY OF TEXAS AT AUSTIN The End