U.S. Army Research Institute 

for the Behavioral and Social Sciences 

Research Report 1848 

Approaches to Managing Future Training 

Charlotte H. Campbell, Roy C. Campbell, 

James R. Grossman, and Christopher R. Graves 

Human Resources Research Organization 

Michael R. Flynn 

Northrop Grumman Information Technology 

February 2006 

Approved for public release; distribution is unlimited.
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Approaches to Managing Future Training 

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Charlotte H. Campbell, Roy C. Campbell, James R. Gossman, 

Christopher R. Graves (HumRRO), Michael R. Flynn

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14. ABSTRACT (Maximum 200 words): 

This report explores issues related to managing adaptable training in the future, when Future Combat Systems (FCS),

embedded training (ET) capabilities, and a full range of personnel management tools are the norm for daily operations. 

The report first discusses the issues associated with management of future training and related current and emerging

Army initiatives, FCS documentation, and previous research findings. The five principal issues concern the vision and

expectation for future training; the tools and information that would be needed; how the tools and information could be

provided using automated channels; what level of automation users will accept; and the nature of near-term solutions. 

The concept for a future training management system, is described through use cases that detail the use of specific

tools and information. Based on analysis of the use cases, technology needs are described in five areas: massive 

database systems, bi-directional reach (both accessing and sending needed information), sophisticated self-learning

search engines, performance support systems and computer-generated simulations powered by artificial intelligence, 

and super-broad bandwidth. We then present a set of detailed recommendations, including priorities, interim solutions 

for near-term development, and directions for further research and development in both training and technology realms. 

15. SUBJECT TERMS 

future training training management embedded training 

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ii 
Research Report 1848 

Approaches to Managing Future Training 

Charlotte H. Campbell, Roy C. Campbell, 

James R. Grossman, and Christopher R. Graves 

Human Resources Research Organization 

Michael R. Flynn 

Northrop Grumman Information Technology 

Armored Forces Research Unit 

Barbara A. Black, Chief 

U.S. Army Research Institute for the Behavioral and Social Sciences 

2511 Jefferson Davis Highway, Arlington, Virginia 22202-3926 

February 2006 

Army Project Number Personnel Performance 

633007A792 and Training 

Approved for public release; distribution is unlimited.

iii
ACKNOWLEDGMENTS

The authors wish to acknowledge the support of the U.S. Army Research Institute for the 

Behavioral and Social Sciences (ARI) in the completion of this project. In particular, we 

recognize and thank Dr. Billy Burnside and Dr. May Throne of the ARI Armored Forces 

Research Unit at Fort Knox. They provided both the vision of future training and a firm grip on 

reality, which together formed the organizing structure for this research and report. We also owe 

a debt of gratitude to Mr. Scott Shadrick, also from the ARI Armored Forces Research Unit, 

whose instruction on the capabilities of future search engines and bandwidth research added 

immeasurably to the discussion of near-term capabilities. 

The authors also gratefully acknowledge the contributions of Mr. Frank Polster, Director 

for Army Training Information Systems at the U.S. Army Training Support Center, and Mr. 

Mitch Bonnett from the Unit of Action Maneuver Battle Laboratory. The generosity with which 

they shared information, ideas, and thinking points was essential in formulating the description 

of issues found in this report. 

Finally, the authors would like to recognize the assistance of the many members of the 

Package 27 (Collective Training) Team, an element of the larger Future Combat Systems 

Development One Team—especially Mr. Bud Dannemiller (Northrop Grumman Information 

Technology), Mr. Bill Holden, and Mr. Clair Conzelman (both from the Human Resources 

Research Organization). Their willingness to explore concepts and their openness to the 

possibilities of future training were invaluable. 

iv
APPROACHES TO MANAGING FUTURE TRAINING 

EXECUTIVE SUMMARY 

Research Requirement: 

As the U.S. Army transformation to the Future Force equipped with the Future Combat 

Systems (FCS) moves forward, system design and development decisions are made daily. In 

parallel, there are training design and development decisions that support the delivery of realistic 

and targeted training, anywhere and anytime, by means of embedded training (ET). Unit 

members will be participating in training while they are seated in their vehicles or at their 

workstations, using the system controls and interfaces just as they would operationally, and the 

training content will be presented through those interfaces. 

Because the system capabilities are being designed now, those who are working on 

managing future training need to address the issues surrounding efficient and effective use of 

embedded training capabilities. The intent of this research of the U.S. Army Research Institute 

for the Behavioral and Social Sciences (ARI), was to identify the issues associated with the 

management of collective training for Future Force Soldiers, leaders, and units, and to design a 

detailed approach for that training management. Of particular interest was the way that future 

embedded training could be adaptable—amenable to changes in response to training needs, with 

minimal programming or laborious administrative effort. 

Procedure: 

Initial project activities consisted of identifying and obtaining materials relevant to the

project objectives. Information on current initiatives within U.S. Department of Defense (DoD) 

was gleaned from conversations with experts and from documentation available on the internet. 

The vast majority of information on FCS capabilities is still evolving, so many of the materials 

examined were draft and preliminary copies. The information was reviewed and analyzed by a 

shifting group of experts, including individuals from ARI, the FCS team focused on collective 

training, and other military and training experts. This analysis consisted of categorizing 

information, identifying consistencies and inconsistencies, and establishing linkages and shared 

capabilities. The information was used to formulate and explore five issues related to future 

training management: 

• Expectations for future training development and delivery, based on FCS network 

services planning and training roles. 

• Types of information and tools that users will need for planning and conducting 

collective training. 

• Methods for providing the information and tools, based on current and emerging Army 

database initiatives and status of computer-generated forces (CGF) and artificial 

intelligence (AI) technologies.

v
• Expectations of the kind of tools (i.e., how much automation) users will accept, 

acknowledging commanders’ need to insert their own expertise and expectations into 

exercises rather than allow an automated system to make decisions and act as the expert. 

• Assessment of successful near-term development of the anticipated tools and capabilities 

and description of the nature of near-term solutions. 

The issues were further explicated by means of informal use cases for future training 

development and execution. Use cases focused on units and unit commanders were used to 

formulate secondary use cases targeting the conceptual future training management system, 

which were decomposed to identify the specific capabilities that would be required. By linking 

the needed capabilities and their purposes to plans and expectations for FCS capabilities and then 

determining the likelihood of technology maturity within the next 10 years, the project team

delineated specific recommendations concerning training management possibilities and needs for 

further research and development. 

Findings: 

The concept for future training management provides a detailed description of how 

collective training exercises can be developed and delivered, given a suite of technologically 

sophisticated services including massive database systems, bi-directional reach (both accessing 

and sending needed information), sophisticated self-learning search engines, performance 

support systems, CGF simulations powered by AI, and super-broad bandwidth. A more realistic 

near-term approach is also described, along with discussion of research and development 

recommendations focused on how the needed information and tools can be provided. The 

findings have necessarily been edited to avoid specific information about FCS decisions that are 

not yet final. 

Utilization and Dissemination of Findings: 

The results of this project can benefit those involved in further definition and development 

of the training management capabilities for the transformation to the Future Force equipped with 

FCS. In addition, the research and development issues provide direction for future work that will 

assist the Army in achieving its stated goals for the Future Force. 

vi
APPROACHES TO MANAGING FUTURE TRAINING 

CONTENTS

Page 

Introduction......................................................................................................................................1 

Issues Associated with Future Training Management.....................................................................2 

What Will Future Training Development and Delivery Be Like? ...........................................4 

 Requirements Analysis for FCS Training Common Components....................................4 

 Battle Command Software Services.................................................................................5 

 Human and Automated Training Roles............................................................................5 

What Kind of Information and Tools Will Users Need?..........................................................6 

How Will the Information and Tools be Provided? .................................................................7 

Combined Arms Training Strategy...................................................................................8 

Digital Training Management System..............................................................................9 

Army Training Information System..................................................................................9 

 Defense Integrated Military Human Resources System.................................................10 

 Computer-Generated Forces and Artificial Intelligence.................................................11 

What Kind of Tools Will Training Managers Accept? ..........................................................13 

What are the Near-Term Solutions?.......................................................................................14 

 Non–Issues .............................................................................................................................14 

The Future Training Management System (Concept) ...................................................................15 

 Method....................................................................................................................................15 

Findings—Primary Use Cases................................................................................................17 

Findings—Secondary Use Cases............................................................................................20 

The Issues and the TMS(C) ...........................................................................................................27 

 Database Systems...................................................................................................................29 

 Bi–Directional Reach .............................................................................................................30 

 Search Engines.......................................................................................................................30 

Performance Support Systems, Computer-Generated Forces Simulation, and 

 Artificial Intelligence ......................................................................................................31 

Improved Bandwidth Capabilities..........................................................................................33 

Summary and Recommendations ..................................................................................................33

vii 
CONTENTS (continued)

Page 

References......................................................................................................................................37 

Appendix A Acronyms and Abbreviations................................................................................ A-1 

List of Tables 

Table 1. Use Case 1 Information Needs and Sources ..............................................................22 

Table 2. Use Case 1 Information Provided to User..................................................................23 

Table 3. Summary of Capability Needs and Research and Development Needs.....................28 

List of Figures 

Figure 1. Use Case 1, showing activities of the commander or training manager in 

preparing for a collective training exercise ................................................................18 

Figure 2. Use Case 2, showing activities of the commander and other unit members 

during conduct of a collective training exercise.........................................................19 

Figure 3. Secondary Use Case 1, showing information flow between the user and 

Training Management System (Concept) during development of an exercise ..........21 

Figure 4. Secondary Use Case 2, showing Training Management System (Concept) 

activity during conduct of an exercise........................................................................25

viii 
APPROACHES TO MANAGING FUTURE TRAINING 

Introduction 

As the U.S. Army transformation to the Future Force equipped with the Future Combat 

Systems (FCS)1

 moves forward, system design and development decisions are made daily. 

Myriad engineers, programmers, and modeling and simulation experts have joined forces with 

military experts to create and integrate the components of the envisioned FCS: a fullynetworked system of systems, enabling rapid and complete sharing of information across 

echelons, leaders, and systems—throughout the full battlespace. The heart of the informationsharing capability will be the system of systems common operating environment (SOSCOE), 

supporting multiple applications that serve a wide array of information demands.2

With these system decisions come training design and development decisions addressing 

the delivery of realistic and targeted training, anywhere and anytime. Again, the SOSCOE is at 

the heart of the capability. The integrated information-sharing environment will make it possible 

to meet all training needs, from individual Soldier training to large scale collective exercises, 

while participants are seated in their vehicles or a tactical command post (TACP), or in high 

fidelity networked training venues known as networked reconfigurable full task trainers 

(NRFTT). This training capability will take advantage of technologies such as massive database

systems, bi-directional reach (both accessing and sending needed information), sophisticated 

self-learning search engines, performance support systems, computer-generated forces (CGF)–

simulated conditions and participants powered by artificial intelligence (AI), and super-broad 

bandwidth, to allow Soldiers and leaders to get the training they need at the exact moment that 

they need it.

The capability to provide this type of training is referred to as an embedded training 

capability. Unit members will be participating in training while they are seated in the vehicles or

at the workstations in the TACP, and they will be using the system controls and interfaces while 

they are in training, just as they would operationally, and the training content will be presented 

through those interfaces. That method of delivery and point of participation is what makes the 

training embedded. The capability is so critical within the FCS development plan that it has been 

designated as a Key Performance Parameter (KPP) for acquisition of the FCS family of systems. 

That, at least, is the plan and the vision. It is more than a little ambitious, depending as it 

does on the use of technologies that are not yet mature or even, in some cases, much beyond the 

drawing boards. For individual training, the delivery will closely resemble computer-based 

instruction or web-mediated instruction. For collective training, where unit members may be 

located on opposite sides of the country or the globe, delivery of exercises presents a much more 

complex picture. Yet, with designation as a KPP, the embedded training capability has become

non-negotiable: it will be realized. In one form or another (yet to be fully comprehended), 

 

1

 Appendix A contains a list of the acronyms and abbreviations used in this report. 

2

 Think of it as Windows® on steroids, where you can pull and push products from all of the applications— 

Microsoft® (MS) Word, MS Excel, Adobe PhotoShop, and so on—with no apparent translation machinations and

complete reliability and accuracy. Then multiply that by about a hundred. That is a pale imitation of the envisioned 

SOSCOE capability. 

1 
training will be delivered to unit members in their vehicles or TACP workstations, through the 

normal user interface, as they use system controls. In some fashion, all training will be available 

anytime, anywhere, and that includes on the vehicles. 

This reality (or vision foreshadowing reality) gives rise to a training management 

imperative: The issues associated with management of future training must be identified, 

examined, and addressed in parallel with the design and development of the systems that will 

provide future training. The system capabilities are being designed now, and the recognition of 

issues and formulation of approaches for the efficient and effective use of embedded training 

capabilities is essential. This will be a continuing task for integrated teams of training design 

experts, engineers, programmers, modelers, and military experts. The unique role for the 

training designers and researchers is to clearly identify the issues and apply relevant research and 

development (R&D) experience to defining resolutions from the training management

perspective. 

To that end, the U.S. Army Research Institute for the Behavioral and Social Sciences 

(ARI) sponsored research to identify the issues associated with the management of collective 

training for Future Force Soldiers, leaders, and units, and to design a detailed approach for that 

training management. Of particular interest was the way that future embedded training could be 

adaptable—amenable to changes in response to training needs, with minimal programming or 

laborious administrative effort. In the course of that research, a host of other issues surfaced 

related to training management, issues that recognize the way in which training is a part of the 

larger system of Army and Department of Defense (DoD) operational requirements and human 

resource management. 

This report is the product of the research. Its purpose is to describe issues and 

recommendations related first to providing adaptable training, and then also to management of 

training in the future, when FCS, embedded training (ET) capabilities, and a full range of 

personnel management tools are the norm for daily operations. The report also discusses 

research issues and procedural questions that will need to be addressed in order to provide 

effective and efficient training for the Future Force. 

The report first discusses the issues associated with management of future training (as well 

as some of the non–issues) and related current and emerging Army initiatives, FCS 

documentation, and previous ARI research findings. The concept for a future training 

management system is then described through use cases. We then present a set of detailed 

recommendations, including priorities, “fall-back” solutions for near– and long–term

development, and directions for further research and development in both training and 

technology realms. 

Issues Associated with Future Training Management 

For about the past 10 years, large-scale collective training events have come to be closely 

associated with (and even confused with) the training support packages (TSPs) that make 

conduct of those exercises possible. In general, the term “training support package,” or TSP, 

refers to all of the information and products that would allow a structured, scenario-based 

2 
exercise to be conducted with some degree of standardization. The U.S. Army Training and 

Doctrine Command (TRADOC) Pamphlet 350-70-1 (Department of the Army [DA], 

Headquarters, 2004) defines a TSP as, “a complete, task-based, exportable package integrating 

training products, materials, and information necessary to train one or more critical tasks” (p. 

65). Scrupulous use of a well-constructed TSP ensures that realistic and doctrinally correct

situations are presented, training objectives are addressed, appropriate observations and 

assessments are made, and targeted feedback is given to the participants (Campbell, Campbell, 

Flynn, Sanders, & Myers, 1995; Gossman, Graves, Mauzy, & Clagg, 2001). When we refer to 

TSPs in this report, we do not mean the training event itself, or a broad program of unit 

training—we mean the package of material that enables conduct of the training, whether 

packaged in digital text and graphics files, on paper, or in some futuristic automated system. 

Ten years ago, TSPs were almost entirely packaged as printed text on paper. As embedded 

training capabilities loom, however, there is widespread recognition that that approach must 

change. In a recent report that synthesizes much of ARI’s research on TSPs for collective 

training, Burnside and Throne (2004) identified five distinct capabilities that embedded training 

systems must provide. These include: 

• rapid tailoring or modification of training events based on the users’ needs, 

performance, and choice; 

• bi-directional reach to and from remote knowledge centers to obtain updates or to 

access and exchange additional products; 

• a simulated operating environment that has the capability to address all aspects of the 

training environment that are not physically present (e.g., terrain, enemy, other friendly 

units, unit personnel), and must therefore be represented synthetically; 

• semi-automated performance measurement for assessment and feedback; and 

• pretests or selection criteria for entry into a training event, based on identified 

individual and collective performance weaknesses. 

The capabilities described by Burnside and Throne (2004) cannot be incorporated as 

features of individual self-contained TSPs that support defined training events, even if the TSPs 

migrate from paper to electrons. They will only be achieved through the framework of a training

management system that integrates the tools and allows them to operate in concert with the 

individual TSPs. The five capabilities are intertwined and thus will require a powerful training 

management system that draws on information resources outside the separate TSPs to adapt 

training events, access information in various repositories, simulate various combination of 

conditions, measure and assess performance, and evaluate training readiness and achievement. 

In formulating a conceptual description of the future training management system, we 

began by listing out the issues. Our initial set was eventually reduced to a critical set of five: 

3 
1. What will future training development and delivery be like, given the technologies and 

mandates?

2. What kind of information and tools will training managers and exercise controllers 

need? 

3. How will the information and tools be provided (or obtained, or produced)?

4. What kind of tools (i.e., how much automation) will the training managers accept? 

5. For tools and capabilities that are far off in the future, what are the near-term solutions? 

These five topics form the organizing construct for this report. This section presents a 

summary of our understanding of the five issues, as well as a short discussion of topics (referred 

to as non–issues) that have already been recognized and addressed and should no longer be 

issues of major concern. 

What Will Future Training Development and Delivery Be Like? 

Without a crystal ball, we can still construct a somewhat cloudy vision of the training 

development and delivery of the future. A primary source of information is the documentation 

that emerges from the FCS design efforts, and particularly from the FCS Training Systems 

Integration Integrated Product Team (IPT). The premise for future training, described in the 

Training IPT’s Training Management Plan, is that collective training must be available on 

demand and will be delivered by means of embedded training capabilities.3

 Our understanding 

of the functioning of future training was shaped by examination of the Training IPT work and 

other products from FCS teams who are working on training issues. The subsections below 

provide short reviews of several areas of FCS R&D: 

• Requirements analysis for FCS Training Common Components (TCC). 

• Software services supporting FCS battle command. 

• Training roles, both human and automated. 

Requirements Analysis for FCS Training Common Components. The Training IPT is 

working with the Program Executive Office for Simulation, Training, and Instrumentation 

(PEOSTRI) to define the basic software components that will support embedded individual and 

collective training. The initial baseline in creation of these TCCs was derived from software 

capabilities developed through Army-sponsored programs. In an analysis of requirements for 

 

3

 The Training Management Plan (FCS Training Systems Integration IPT, 2005) is classified by Boeing and SAIC, 

the FCS Lead Systems Integrator (LSI), as Distribution D - Distribution authorized to the Department of Defense 

and U.S. DoD contractors (Administrator or Operational Use). Those wishing to view the plan should contact the 

LSI. Additionally, a more detailed version of this report, which would also be classified as Distribution D, was 

prepared for ARI.

4 
software components that will support training, a number of tools and functions are described.4

 

The definitions and the longer descriptions conjure up a rich descriptive picture of embedded 

training capabilities. However, they are at this stage very general and largely from the point of 

view of the system, not the human user.

Battle Command Software Services. Additional FCS design information is found in 

descriptions of the Battle Command Software Services (BCSS).5

 Like the TCCs, these services 

are applications that are available through the SOSCOE. The descriptions of the services and 

subservices (the features and capabilities of each) are not constrained by considerations of what 

is possible today. Neither are they yet very specific in how these services will be provided. 

However, as noted by Mauzy, Flynn, Dannemiller, and Gossman (2003), services and systems 

that support performance during operations should also be incorporated in training. This is more 

than a matter of “train as you fight.” In fact, such performance support services can also be 

useful in designing training events and constructing TSPs as well as in conducting the training. 

Human and Automated Training Roles. In addition to descriptions of software services 

and components, the human role in future training is being defined. The definition of training 

roles is seen as essential for FCS software developers to understand the needed features and 

capabilities of the embedded training systems, as well as the demands that will be placed on the 

SOSCOE. A role is not a duty position, nor is it inextricably linked to any particular duty 

position. Rather, on any given day, one of any number of individuals, from platoon leaders to 

battalion commanders and staff, could be designated to perform the role. In general, the 

functions currently performed by humans that will require assignments in the future include: 

• a training manager, who makes decisions about what training will be conducted; 

• an exercise controller, who actually monitors and directs a training exercise; and 

• a CGF operator, who controls the behaviors of entities in simulation.6

The still-evolving working definitions contain considerable detail on the specific actions 

that would need to be performed. A notable feature is that, at this point in time, the roles are 

fully anthropomorphic, that is, expressed as though performed by humans. Given the rapid 

advances being made in the use of broadband networks, complex databases, intelligent agents, 

and collaborative tools, many of the activities that are at present assigned to humans should 

eventually be relegated to automated systems. This is not a startling insight, nor inconsistent

with expectations of the Training IPT; it merely reflects the difference between realistic nearterm planning (for the 2012-2016 timeframe) and more fantastic long term aspirations. 

 

4

 The Requirements Analysis for FCS Training Common Components (FCS Training Common Component

Technical Integrator, 2004), describes the planned software assets. The Requirements Analysis has not been

designated for open distribution. Those wishing to view the analysis should contact the LSI. 5

 Descriptions of the Battle Command Support Services are still under development and have not been designated 

for open distribution. Those wishing to view descriptions should contact the LSI. 6

 Definitions of the roles and their specific responsibilities are still under development and have not been designated 

for open distribution. Those wishing to view descriptions should contact the LSI. 

5 
The FCS Training IPT has made impressive progress in translating the FCS Operational 

Requirements Document ([ORD] Unit of Action Maneuver Battle Laboratory, 2002) into 

increasingly detailed descriptions of services and human roles, so that engineers, programmers, 

military experts, and training experts can have a common understanding of the necessary design 

and development products. The FCS documentation is convergent overall, with only minor 

inconsistencies that are likely an artifact of semantics rather than internal conflicts or gaps in 

planning. Although the design and development, and the documentation itself, are still works in 

progress, the degree of convergence suggests that the information is reliable and sufficiently 

mature to serve as the basis for further analysis. 

Armed with those descriptions of the FCS operational and training system requirements, 

technology services, and roles of humans interacting with services, we formulated informal use 

cases that portray the training development/delivery/management system in operation. As the 

Training IPT continues its efforts, we expect that they will further explicate the descriptions into 

specific elements by means of detailed, formal use cases. Our initial version of these products is 

contained in the next major section, following discussion of the other issues. 

What Kind of Information and Tools Will Users Need? 

Among the users and uses that were culled from review of the various products and think 

pieces, the following (broad but not particularly deep) descriptions of users and uses emerged: 

• For individual Soldiers (including everyone from the Private to the Command Sergeant 

Major, from the Second Lieutenant to the General Officer) who are interested in their 

own training—the capability to see what training they have completed, what training 

they could do next, and what opportunities are available for those requirements; a way 

to sign up for or schedule individual training; options for additional non-required 

individual training. 

• For unit leaders and their staffs—the capability to view data on individual and unit 

readiness and plan further training by reviewing training completed, progression 

specified in their Combined Arms Training Strategy (CATS), and opportunities are 

available for training; tools to modify their CATS as their missions change; a way to 

sign up for or schedule unit training; options for additional non-required training. 

• For training facility managers—tools for monitoring use of live training areas, 

constructive/virtual simulation facilities, and NRFTT sites, reviewing and adjusting 

schedules with unit leaders, scheduling routine or situation-driven maintenance of their 

facilities. 

• For organizations engaged in training and doctrine development—the capability to 

monitor currency and quality of training materials and training support packages, make 

any necessary updates with very short turnaround time, provide rubrics on skill decay, 

prepare and incorporate optimal schedules for refresher training, construct baseline 

tables of organization and equipment (TO&E) training strategies (e.g., CATS) and 

formulate options within those strategies. 

6 
• For personnel managers—the ability to monitor individual training opportunities and 

availability by means of both regular and on-demand reports of fill rates, seats 

available, requirements for training; the capability to forecast training availability 

through “what if” simulations; tools to optimize personnel assignments based on 

proficiency; tools to monitor completion of required training and professional 

development for promotion purposes. 

The first three of those users and uses concern planning and scheduling of training. The 

fourth one, referring to the process for making prepared training modules and TSPs available for 

users, clearly belongs under training management as a sort of planning function, although it is 

decidedly different from the first three. The last one addresses use of archived training data for 

non-training purposes, and is only tangentially a training management concern. Although these 

requirements address a number of uses and users, they should be seen as parts of a single 

multipurpose system: all of the functions described above are intertwined and will need to share 

information. 

Conspicuously missing in those discussions and plans is a description of how the training 

of the future can be provided anytime, anywhere, and how to make it readily adaptable by 

Soldiers and units, as described in various ARI research products (e.g., Campbell & Holden, 

2001; Gossman, Flynn, & Breidenbach, 2004; Throne & Burnside, 2003). Thus, another use was 

noted: 

• Additionally, for individuals, small groups, and units—a means for on-demand delivery 

of training, over the (future) network, as interactive multimedia instruction, small group 

instructor-led instruction (synchronous or asynchronous), small group multiplayer 

exercises, or unit collective simulation-based exercises; facility for producing tailored 

exercises and accompanying TSPs to target specific tasks, scenarios, and participating

individuals or units; capability to modify existing exercises quickly (even “on the fly”); 

support for conducting training with less than full unit participation; automated data 

archiving; facility for leaders to modify training events and annotate archived data for 

unit needs. 

All of these users and uses have a vested interest in the training management of the future. 

However, our primary focus in this effort is on collective training—development, delivery, 

feedback, and data archiving. Thus, the uses and users of interest are the unit leaders planning 

collective training and the units participating in adaptable collective training. The use cases 

described in the next major section were based on these initial descriptions. 

How Will the Information and Tools be Provided? 

The emerging FCS documentation, some of which was described above, gives a general 

overview of what software services are being planned to support training development, delivery, 

and management. The description begs the question of where the information that those services 

use will come from. Several prime sources are likely candidates, including the Army Training 

Information Architecture (ATIA), the Common Training Instrumentation Architecture (CTIA), 

and the Unit Training Management Component (UTMC; sometimes referred to as the Unit 

7 
Training Management Configuration). These are large, complex database systems that contain 

doctrinal information about individual and collective tasks; unit information such as their CATS 

and Mission Essential Task List (METL); individual and unit training history; linkages to other 

databases that permit scheduling and resource planning; and semi-automated tools to assist 

training personnel in managing the information for their own training needs. Clearly, one 

expectation is that the extensive research and development that has already been focused on tools 

for training management should not be scrapped. 

The resources that we examined yielded descriptions of a core set of databases that may 

serve as the source for future training management. Most of the effort is focused on immediate 

and near-term solutions, designed to address the frustrations experienced today with the 

technologies available today. Nonetheless, four database systems seem likely to be useful for 

Future Force training, as they are now (or should be in the near future) for Current Forces. They 

are described briefly below. As concerns tools, some of those are integral to the database 

systems. Others, though, will make use of highly realistic AI and CGF. A brief summary of the 

current and emerging developments on AI and CGF is also presented below. The ensuing 

discussions address: 

• the CATS program. 

• the Digital Training Management System. 

• the Army Training Information System. 

• the Defense Integrated Military Human Resources System. 

• advances in CGF and AI. 

Combined Arms Training Strategy. One such initiative is the CATS program, the Army’s 

overarching strategy for current and future training of the force. Its basis is a series of branchproponent unit and institutional strategies describing training events, event frequencies, and 

resources required to train to standard. These strategies describe how the Army will train the 

total force to standard through self-development, institutional training, and in units. They also 

document the quality of and justification for all training resources required to execute the 

training. The automated CATS module (currently a part of the Automated Systems Approach to 

Training [ASAT]) allows training developers to produce individualized training strategies using 

automated tools. It identifies training events using a crawl–walk–run training methodology; 

provides a conceptual training calendar that lays out the training events under perfect training 

conditions; and outlines the recommended number of times tasks should be trained during one 

training year based on an interval that considers key personnel turbulence, skill decay, mission 

changes, and task complexity. For every training event, there are details that outline the purpose, 

outcome, recommended training audience, means (including training aids, devices, simulators, 

and simulations, or TADSS), estimated event duration, critical training gates (i.e., prerequisite 

training or readiness criteria), and execution guidance. 

8 
Digital Training Management System7

. A promising tool that builds on the information 

found in the CATS is the Digital Training Management System (DTMS). The DTMS is a 

customized commercial-off-the-shelf (COTS) product provided by nFocus Software®. An 

Information Paper describes the system as “...a user-friendly web-based unit training 

management system for units at all levels to track and manage their unit training ... [It is] 

optimized to support unit training management from company to brigade level” (Collective 

Training Directorate, 2005a). Through the DTMS, unit leaders and trainers can detail the unit’s 

METL, personnel, and modified tables of organization and equipment (MTO&E); view CATS 

and schedule training; review training assessments and after action reviews (AARs); and both 

examine and add to a library of references. According to the DTMS training site (Collective 

Training Directorate, 2005b), it is (or is going to be) linked in real time to a host of other 

databases, including: 

• Personnel systems (e.g., EMilpo–Electronic Military Personnel Office, ITAPdB–

Integrated Total Army Personnel Database, SIDPERS–Standard Installation/Division 

Personnel System), 

• Training facility and resource systems (e.g., RFMSS–Range Facility Management 

Support System, ATRRS–Army Training Requirements and Resources System, 

TAMIS-R–Training Ammunition Management Information System-Redesign, TESS–

Tactical Engagement Simulation System), and 

• Task and training information systems (e.g., CALL–Center for Army Lessons Learned, 

ATIS–Army Training Information System, ITRS–Individual Training Readiness 

System). 

Army Training Information System. As it turns out, the DTMS is subsumed under one of 

the systems that are shown as a supporting database: the ATIS, which is overseen by 

TRADOC’s Army Training Support Center (ATSC). The ATIS is “...a system of systems that 

brings together myriad functions in one comprehensive Army training system. [The] ATIS uses 

the [World-Wide Web] to access an all inclusive digital library and data repository through a 

suite of software applications including: training development; individual training management; 

unit training management; and common core services available to all through the Soldier 

Training Homepage” (Total Army Distance Learning Program [TADLP], 2005). Functions and 

information currently found in ASAT, such as task analysis products, are to be migrated to ATIS 

(TADLP, 2005). 

The key software applications of the system are (or will be): 

• Training and Doctrine Development Tool (TDDT)—used by the training development 

community to create and develop the source doctrine and training materials required to 

develop training products. 

 

7

 The “Digital Training Management System” (DTMS) is a database product that satisfies most, if not all, of the 

requirements for the UTMC. 

9 
• Army Learning Management System (ALMS)—delivers standardized individual 

training to Soldiers, providing a fully automated, seamless and web-accessible training

and education network; provides users a common “tabbed” start page from which they 

can access the other configurations, materials in the Reimer Digital Library and 

Common Core Services of the data repository. 

• The UTMC—provides the functionality to analyze, manage, plan, conduct, evaluate, 

and provide feedback on unit training and is, in fact, the defining requirement for the 

DTMS. 

Defense Integrated Military Human Resources System. To round out the picture, there is 

an even more overarching system: The Defense Integrated Military Human Resources System

(DIMHRS). The DIMHRS is a congressionally-mandated program which crosses all military 

services. It consists of three functional areas, named Personnel and Pay, Manpower, and 

Training. Its origins are in a realization that each military service (Army, Navy, and Air Force) 

has had separate systems, which comprise over 160 documented redundant systems, databases, 

and interfaces—and that was only for personnel and pay functions. This resulted in significant 

gaps in personnel management, including the inability to track active and reserve status changes, 

account for personnel in theater, maintain consistent service records, and ensure adequate 

security of personal information. As envisioned, DIMHRS will provide real-time accurate 

information to Soldiers, human resources professionals, and commanders from non-redundant 

databases. 

The personnel and pay functions have been captured in the subsystem of DIMHRS known 

as the Enterprise Human Resources System, which is now in initial fielding beginning with the 

Army and to be followed by the Navy and Air Force. Attention has turned to the manpower 

function, and the training function will follow. Examination and needs/gap analysis of CATS, 

DTMS, ATIS, and all of the systems and interfaces to which those programs are linked should 

prove to be an interesting and informative exercise. At this point, there is little information 

available about the purpose or goals of the training subsystem. 

It is apparent that training management systems are on the minds and drawing boards of 

many Army and DoD decision-makers. Yet these are not the visionary designs that will push 

technology development to address future needs. Advocates of CATS, DTMS, and TDDT will 

point out, accurately, that users can construct programs of instruction and exercises, using the 

tools provided. This is not the same as obtaining training support on-demand—that is, with a 

minimum of training developer manipulation of database information. This point will be 

discussed more fully in the next section. As of today, ATIS is probably the prime candidate for 

addressing the needs for training management. It may someday be absorbed into DIMHRS, but 

at this point it has at least some functionality, while the training component of DIMHRS has not 

entered the analysis phase. According to the ATIS proponent, the ATSC, ATIS will provide the 

architecture to allow personnel, resource, training and doctrine development, and other 

applications to be able to cross-reference and interact with one another (TADLP, 2005). It is 

intended to be the overarching tool for integrated training management. 

10 
 Computer-Generated Forces and Artificial Intelligence. Two related aspects are involved 

in considering CGF progress: the visual representation and the behavioral representation. The 

visual representation, as the name suggests, refers to the apparent realism of objects in 

simulation, which is conveyed by physical details, shadows, and perspective that change to 

correspond with the point of view of the person viewing the simulation. Behavioral 

representation refers to how objects move and react to movements of other objects. For humans, 

for example, it includes walking and running, moving around obstacles, turning around to look at 

or listen to things, falling when hit with a killing projectile (e.g., a bullet). For vehicles, the 

behavioral representations include speed of movement over various terrain types, blowing up 

when hit by a missile, and blowing up differently when an improvised explosive device (IED) 

goes off. Buildings and other terrain features don’t move, but they do show different effects 

when damaged in different ways. It is in consideration of behavioral representation that AI 

enters the discussion, as a way to more accurately and quickly portray the behaviors. 

The areas of CGF and AI (and a related area, human behavioral representation) are not 

new. However, despite unrelenting theorizing and developments, they continue to be important 

topics for researchers. Conference schedules for gamers, military system developers, and 

medical training experts (among others) are heavy with papers and presentations on faster and 

more realistic techniques, processes, and applications. Review of the topics of recent CGFrelated research and presentations, all of which allude to the use of some form of AI, 

demonstrates the relevance of ongoing CGF and AI R&D to ET delivery. A sampling of those 

topics includes: 

• Use of Performance Measurement Objects (PMO), which represent actors, behavioral 

data, and measurement methods and support the real-time requirements of intelligent 

agents, human observer/instructors, and distributed performance assessment processors 

(Stacy, Freeman, Lackey, & Merket, 2004). 

• Standardizing CGF behaviors so that composite behavior developed for one simulation 

can be reused in another, by having the composite behavior make reference to primitive 

behaviors that are functionally equivalent in the two simulations (Gerber & Lacey, 

2004). 

• Formalized behavior models for CGF entities that allow quick creation of new 

behaviors and multiple variants and easy modification of existing behaviors (Stottler, 

Lackey, & Kirby, 2004). 

• Integrating physics-based damage effects in real-time urban simulations to represent the 

visual aspects of damage and permit calculation of the extent of damage and personnel 

casualties (Mann, York, & Shankle, 2004). 

• Use of dynamic procedural tactics, wherein AI tailors tactics to the situation and terrain 

at hand using on-the-fly algorithms and dynamic inputs (Straatman, van der Steeren, & 

Beij, 2005). 

11 
• Methods for determining line-of-sight in complex simulated environments (University 

of North Carolina at Chapel Hill, 2005). 

Visual representation is already very good, especially for objects (as opposed to people), 

and continues to improve. Behavioral representation also continues to improve with respect to 

every aspect of a virtual simulation—natural and man-made terrain features, vehicles, humans 

and animals, and munitions. In trying to extend virtual simulations to distributed training, the 

implementation of realistic behavioral representations is frequently brought up short by 

considerations of bandwidth. 

In practice, AI is more distinct from CGF than CGF is from AI—that is, there tend to be 

more scientific papers and presentations that address AI apart from CGF than the converse. Still 

there exists some confusion regarding the definition of AI. According to the “Artificial 

Intelligence Frequently Asked Questions (FAQ)” website (Crabbe, Dubey, & Kantrowitz, 2004), 

the phrase is so broad that people have found it useful to divide AI into two classes: strong AI 

and weak AI. Strong AI makes the bold claim that computers can be made to think on a level (at 

least) equal to humans and possibly even be conscious of themselves. Weak AI simply states 

that some “thinking-like” features can be added to computers to make them more useful tools 

(e.g., expert systems, drive-by-wire cars, speech recognition software) Subtopics within the 

realm of AI include both problems and solutions: 

• Automatic programming—the task of describing what a program should do and having 

the AI system ‘write’ the program. 

• Bayesian networks—a technique of structuring and inferencing with probabilistic 

information (part of the “machine learning” problem). 

• Knowledge engineering/representation—turning what we know about a particular 

domain into a form in which a computer can understand it. 

• Machine learning—programs that learn from experience or data. 

• Natural language processing (NLP)—Processing and (perhaps) understanding human

(“natural”) language. Also known as computational linguistics. 

• Neutral networks (NN)—The study of programs that function in a manner similar to 

how animal or human brains do. 

• Planning—given a set of actions, a goal state, and a present state, decide which actions 

must be taken so that the present state is turned into the goal state. 

• Speech recognition—Conversion of speech into text. 

• Visual pattern recognition—The ability to reproduce the human sense of sight on a 

machine. 

12 
These are the very capabilities and technologies that will be needed to support simulationbased training, as will be shown later. The technologies are not just theoretical; they are being 

used broadly today for such developments as: 

• Financial software, which is used by banks to scan credit card transactions for unusual 

patterns that might signal fraud. 

• Applications of expert systems/case-based reasoning to create a computerized leukemia 

diagnosis system that does a better job checking for blood disorders than human 

experts. 

• Machine translation software, developed in the 1970s, that translated natural language 

weather forecasts between English and French. 

• Deep Blue, the first computer to beat the human chess Grandmaster. 

• Physical design analysis programs, such as for buildings and highways. 

• Fuzzy controllers in dishwashers, clothes dryers, and other common appliances. 

Even with a very shallow understanding of CGF and AI, it is obvious that much has been 

accomplished and much remains to be done. However, the possibilities are directly relevant to 

the planning for adaptive training management. 

This information on current and near-term-future database systems and CGF/AI was used 

in formulating the use case analyses described in the next major section. 

What Kind of Tools Will Training Managers Accept? 

The FCS documentation and database initiatives described above present a curious mix of 

automated tools and human intervention. While the FCS technologies will likely be able to take

over much of the time-consuming effort of planning training events and preparing TSPs, most of 

the documentation still indicates that the Training Manager or Exercise Controller will prepare 

tactical materials for the TSP, make decisions about scenario events, determine initiating 

conditions for the exercise, control enemy and other friendly forces during the exercise, and 

prepare and deliver the AAR. The capability for systems to do the work for individuals may not 

mean that individuals—especially commanders—will be willing to relinquish control of their 

training events. This is more than a matter of job protection or mistrust of systems’ abilities to 

do as well as humans (although there may be some of that). It is at least partially the very proper 

responsibility that the commanders feel for the training of their units and unit members, and the 

accountability for readiness to their higher-ups. It may also be a lingering reluctance to lose 

hands-on control—and this is the same danger that commanders encounter in all of their 

networked operations: they have the capability to micromanage, and the appropriate balance 

between knowing and controlling continues to be an issue. 

13 
In the next major section, where the use cases are expounded, we have chosen the route on 

which the commander trusts the system completely to make many of the decisions in planning, 

preparing, and delivering the training, but also has considerable freedom to tweak the parameters 

of a training event. 

What are the Near-Term Solutions? 

This is the crux of the matter. Given the current status of work on FCS, the timeline for 

FCS development and production, the current and emerging initiatives, and the pace of

technology development, it will be important to forecast the likelihood of actually realizing the 

plans for collective training using ET capabilities. This issue and some possible answers will be

discussed and presented in a later section, titled “Issues and Recommendations.” 

Non–Issues 

We also identified a number of decision and planning points that have either already been 

addressed or that are, in the long run, relatively unimportant in determining the scope and 

structure of future training management. However, because they contribute to our understanding 

of the issues by helping to restrict the scope, we describe them briefly here. They include: 

• What will the different kinds of training be? All of the FCS documentation refers to 

simulation–based training, individual interactive courseware (ICW) or interactive 

multimedia instruction (IMI), and interactive electronic technical manuals (IETMs). 

Our specific concern here is collective training—that is, simulation–based. The 

simulations are more specifically characterized as live, virtual, or constructive, or some

combination. With embedded training capabilities, distinctions between live, virtual, 

and constructive will blur even more than they do today, and all exercises will 

necessarily be a combination. The more pertinent question concerns whether the 

training be conducted on the FCS platforms completely in training mode (stationary) or 

somewhere else? The “somewhere else,” which may include the NRFTT or moving 

(live) FCS vehicles, requires additional resources that the embedded training system

cannot itself supply, which requires some additional coordination. 

• How will the training products be adapted from embedded training versions to run on 

NRFTT or other means of delivery? They won’t. Everything will be constructed in 

such a way that the various systems are entirely compatible. The embedded training 

system that works for the FCS platform will work in exactly the same way for NRFTT, 

desktops, personal digital assistants (PDAs), and other devices, at least as far as the 

transmission of electrons is concerned. 

• Who will be the human training manager (HTM)? As described earlier, the training 

manager role will be assumed by different people, including unit commanders or 

members of their staffs, for different training events. While that decision may be 

reversed someday, it is a reasonable course of action, given the fact that commanders 

are in fact responsible for unit performance. The two other roles—exercise controller 

14 
and CGF operator—should eventually be very minor roles as the automated training 

management takes over those functions. 

• How will the HTM have time and expertise to build the training event and the TSP? 

Given the projected sophistication of the FCS software services and other performance 

support systems, the HTM would need very little in the way of formal knowledge of 

how to construct the training, but would need to understand the unit’s training needs 

and have a normal complement of tactical and operational expertise.8

• How will the HTM know what to train? Currently, task information is found in ASAT 

and decisions on what to train are based on the unit METL and estimates of readiness. 

In essence, that process will continue, although automated tools should make the 

information on optimal training schedules and current unit proficiency more available 

and more accurate. The task information will have been upgraded and migrated to 

ATIS. 

• How will the HTM get the latest training information and content onto FCS platforms? 

The SOSCOE, with networked links back to databases and repositories and the Home–

Station Operations Center (HSOC), will access the information when needed. Separate 

versions will not be resident on individual FCS platforms. 

The Future Training Management System (Concept) 

The vision for future management of training is for the most part being painted with a 

fairly broad brush. In order to develop a concept for future training management, we brought 

together the preliminary findings on FCS training designs, information and automation needs, 

available tools and databases, and commander preferences, and used the assembled information 

to paint the more detailed picture. We focused on collective training, following it through the 

full process of analysis, design, development, delivery, and evaluation, as defined by the Army’s 

Systems Approach to Training in TRADOC Regulation 350-70 (DA, 1995). 

Our process is described below, followed by a description of the interim and final 

conclusions. 

Method 

We began with a use case approach to identifying training management issues and 

describing the training management system of the future. Our assumptions concerning the 

available technologies were very optimistic (and were stated earlier): massive database systems, 

bi-directional reach, sophisticated self-learning search engines, performance support systems, 

CGF–simulation of conditions and participants powered by AI, and super-broad bandwidth. Not 

all of these are likely to be ready for use by the time the FCS is fielded (around 2016-2020). 

Nonetheless, we posited these capabilities. 

 

8

 We would say “a full complement of tactical and operational expertise” except that that would obviate some of the 

need for training. 

15 
Using the information gathered and reviewed, we prepared informal use cases for two of 

the major aspects of training management described above: (1) planning and scheduling of 

collective training events, and (2) conduct of collective training (including data archiving). 

These two use cases included the activities described for the three FCS training roles (training 

manager, exercise controller, and CGF operator) although the correspondence was not 

specifically delineated. In all cases, the principal figure (the actor) was the human, and the 

supporting actor was the training management system and interface. By focusing first on the 

human user, we explored capabilities and features in a user–centric, rather than system–centric, 

mode. 

A note on use cases: We followed the guidance from Larman (2002) in developing these 

use cases. He describes what he refers to as casual-style “black box” use cases; they do not 

describe the inner workings of the system, but rather define system responsibilities in relation to 

user needs. He also suggests a presentation comprising the primary path, with extensions to 

describe branching or alternate activities. In our use cases, the extensions are shown in brief 

style, which includes even less detail than casual style. 

We then analyzed those use cases to prepare secondary use cases, where the principal actor 

was the training management system and interface. This had the effect of focusing attention on 

what actions the training management system should be able to perform and how it will be able 

to perform those actions, in support of the user. These elaborated use cases are closer to the 

“fully dressed” format (Larman, 2002). 

From secondary use cases, we extracted training management system actions, and 

organized them according to type of action (e.g., database look-up, report preparation). The 

product of this activity was a definition of training management system requirements in terms of 

types of capabilities and features, as well as specific capabilities and features. We then worked 

backwards to identify critical “what–if” situations, possible workarounds, priorities for 

development that consider both criticality and realistic development pace, and recommendations. 

To make the discussion easier to follow, we will henceforth refer to the future training 

management system as the TMS(C), which stands for training management system (concept).9

 

The principal user of the TMS(C) is the training manager (the unit commander or his designated 

representative) during planning and development of the training event, and the unit itself during 

conduct of the exercise. We will use the pronoun “he” when referring to the user, with full 

understanding that current and future users may be either male or female. 

All of the interactions between the user and the TMS(C) will be mediated through the 

Warrior Machine Interface (WMI), which the user employs habitually to send and receive 

information. The WMI is the portal for links to a vast array of databases via the SOSCOE. The 

TMS(C) will have numerous integrated routines to allow it to access, analyze, compile, report, 

and archive data, and all of this will be completely transparent to the user. The TMS(C) will 

rarely print anything, although individuals can. Instead, most presentation of information will 

occur via the user’s WMI. 

 

9 We are reluctant to propose a catchy name and acronym, for fear it will stick and the conceptual description will be

thought of as an actual system and begin to take on a life of its own. 

16 
Findings—Primary Use Cases 

The first use case, covering activities in preparing for a collective exercise (including 

analysis of training needs, design of the training event, and development of the TSP) is 

diagrammed in Figure 1. The actual conduct of the training event (including both 

implementation and evaluation) is captured in the second use case, depicted in Figure 2. 

Constructing the two use cases enabled us to identify a number of facts and assumptions 

about the user and the TMS(C). Those concerning the TMS(C) required further analysis by 

means of the secondary use cases mentioned earlier. Those concerning the user—commander, 

training manager, or unit members—were primarily concerned with the decisions that he must 

make or would want to make, and were entirely derived from Use Case 1 (preparation). These 

included: 

• Type of training—collective exercise (unit or small group) or individual training. 

(Note: For the remaining considerations concerning the user of the TMS(C), we will 

follow the thread of collective exercises, not individual training.) 

• Training venue—on stationary FCS platforms, in NRFTT, on moving FCS systems 

using augmented reality, or using other common devices (desktops, Notebooks, PDAs, 

etc.) 

• Level of participation—full or with specified participants absent. 

• Preparation activities—conducting prerequisite individual or collective training. 

• Thumbprint—multiple opportunities to provide commander’s input to tailor the training 

conditions and AAR, and to annotate the archived data, concerning any aspect of the 

tactical situation, exercise intensity, scenario events and timeline, discussion points and 

priorities, analysis of performance data, readiness assessment, etc. 

• Level of assistance desired during the exercise—multiple selections including 

remediate poorly performing Soldiers one-on-one, provide unobtrusive in-stream

coaching, perform pause-and-coach, and make in-stream adjustment to exercise 

conditions to reflect unit proficiency (as opposed to a straight run-through of the 

exercise with no assistance other than what the FCS BCSS would provide during actual 

operations). 

• Other human support—whether or not to have a live Observer/Controller (O/C) for 

coaching and feedback. 

17 
...prepares for the event, individually and with staff and unit 

members, taking advantage of access to additional material via the 

reach capability through WMI (M)

...views training events recommended on the basis of current 

readiness status for the unit and key personnel on METL tasks and 

supporting individual tasks, as well as upcoming scheduled training 

activities for the unit and higher echelons (A)

...selects the type of training 

event (individual, collective, 

echelon, mission) (B)

...reviews options for training specific to the selected training event 

that should be conducted first, based on assessment of unit and 

individual readiness (C)

...decides to schedule or dismiss

the suggested training (D)

...reviews recommended training venue(s) (stationary FCS, NRFTT,

moving FCS, PC–type) and training date(s) (based on 

preparation needed and other events in the unit’s calendar) (E)

...selects a venue and date (F)

...reviews options for terrain, enemy type and capabilities, time to 

spend in the training event, intensity or difficulty level of the 

conditions and scenario events, environmental conditions (day/night, 

weather), level of unit participation (all or some), level of automated 

assistance (G)

...examines a representation of the exercise in four dimensions

(spatial 3-dimensional plus time) that also indicates the task steps, 

and reviews a summary of the TSP (J)

...requests changes to reflect his

intent (or approves in toto) (K)

...gives the instruction to 

distribute the readahead 

package (individual and unit 

preparation) (L)

...gives the instruction to notify

participants and arrange for 

resources (I)

...selects parameters values or

accepts defaults (H)

The User identifies himself through the WMI and ...

Note: Acronyms for Figure 1.

WMI = Warfighter Machine Interface NRFTT = Networked Reconfigurable Full Task Trainers

METL = Mission Essential Task List PC = personal computer

FCS = Future Combat Systems TSP = Training Support Package

...prepares for the event, individually and with staff and unit 

members, taking advantage of access to additional material via the 

reach capability through WMI (M)

...views training events recommended on the basis of current 

readiness status for the unit and key personnel on METL tasks and 

supporting individual tasks, as well as upcoming scheduled training 

activities for the unit and higher echelons (A)

...selects the type of training 

event (individual, collective, 

echelon, mission) (B)

...reviews options for training specific to the selected training event 

that should be conducted first, based on assessment of unit and 

individual readiness (C)

...decides to schedule or dismiss

the suggested training (D)

...reviews recommended training venue(s) (stationary FCS, NRFTT,

moving FCS, PC–type) and training date(s) (based on 

preparation needed and other events in the unit’s calendar) (E)

...selects a venue and date (F)

...reviews options for terrain, enemy type and capabilities, time to 

spend in the training event, intensity or difficulty level of the 

conditions and scenario events, environmental conditions (day/night, 

weather), level of unit participation (all or some), level of automated 

assistance (G)

...examines a representation of the exercise in four dimensions

(spatial 3-dimensional plus time) that also indicates the task steps, 

and reviews a summary of the TSP (J)

...requests changes to reflect his

intent (or approves in toto) (K)

...gives the instruction to 

distribute the readahead 

package (individual and unit 

preparation) (L)

...gives the instruction to notify

participants and arrange for 

resources (I)

...selects parameters values or

accepts defaults (H)

The User identifies himself through the WMI and ...

...prepares for the event, individually and with staff and unit 

members, taking advantage of access to additional material via the 

reach capability through WMI (M)

...views training events recommended on the basis of current 

readiness status for the unit and key personnel on METL tasks and 

supporting individual tasks, as well as upcoming scheduled training 

activities for the unit and higher echelons (A)

...selects the type of training 

event (individual, collective, 

echelon, mission) (B)

...reviews options for training specific to the selected training event 

that should be conducted first, based on assessment of unit and 

individual readiness (C)

...decides to schedule or dismiss

the suggested training (D)

...reviews recommended training venue(s) (stationary FCS, NRFTT,

moving FCS, PC–type) and training date(s) (based on 

preparation needed and other events in the unit’s calendar) (E)

...selects a venue and date (F)

...reviews options for terrain, enemy type and capabilities, time to 

spend in the training event, intensity or difficulty level of the 

conditions and scenario events, environmental conditions (day/night, 

weather), level of unit participation (all or some), level of automated 

assistance (G)

...examines a representation of the exercise in four dimensions

(spatial 3-dimensional plus time) that also indicates the task steps, 

and reviews a summary of the TSP (J)

...requests changes to reflect his

intent (or approves in toto) (K)

...gives the instruction to 

distribute the readahead 

package (individual and unit 

preparation) (L)

...gives the instruction to notify

participants and arrange for 

resources (I)

...selects parameters values or

accepts defaults (H)

The User identifies himself through the WMI and ...

Note: Acronyms for Figure 1.

WMI = Warfighter Machine Interface NRFTT = Networked Reconfigurable Full Task Trainers

METL = Mission Essential Task List PC = personal computer

FCS = Future Combat Systems TSP = Training Support Package

Figure 1. Use Case 1, showing activities of the commander or training manager in preparing for 

a collective training exercise. 

18 
...receive guidance on 

additional training and exercises

to reinforce skills (W)

Unit members enter training mode through WMI and ...

...see and hear realistic

scenario conditions and cues

through WMI and other FCS 

channels (N)

...see and hear realistic

indicators of enemy and other 

friendly unit reactions to unit’s

actions (O)

...see and hear realistic

physical effects of actions

(P)

...use WMI and other FCS 

capabilities (including reach and 

performance support systems) to 

perform mission tasks (Q)

...receive unobtrusive

individual and unit 

coaching as

appropriate (R)

...receive one-on-one 

remedial instruction 

while simulated 

individuals participate

in the exercise in their 

place (S)

...experience scenario 

actions and cues appropriate 

to their demonstrated 

readiness for challenging 

training (T)

...experience pause in 

exercise with collective

remedial instruction as 

needed (U)

...participate in AAR 

discussions (V)

...receive feedback reports on 

their individual task

performance (X)

Note: Acronyms for Figure 2.

WMI = Warfighter Machine Interface

FCS = Future Combat Systems

AAR = After Action Review

...receive guidance on 

additional training and exercises

to reinforce skills (W)

Unit members enter training mode through WMI and ...

...see and hear realistic

scenario conditions and cues

through WMI and other FCS 

channels (N)

...see and hear realistic

indicators of enemy and other 

friendly unit reactions to unit’s

actions (O)

...see and hear realistic

physical effects of actions

(P)

...use WMI and other FCS 

capabilities (including reach and 

performance support systems) to 

perform mission tasks (Q)

...receive unobtrusive

individual and unit 

coaching as

appropriate (R)

...receive one-on-one 

remedial instruction 

while simulated 

individuals participate

in the exercise in their 

place (S)

...experience scenario 

actions and cues appropriate 

to their demonstrated 

readiness for challenging 

training (T)

...experience pause in 

exercise with collective

remedial instruction as 

needed (U)

...participate in AAR 

discussions (V)

...receive feedback reports on 

their individual task

performance (X)

Note: Acronyms for Figure 2.

WMI = Warfighter Machine Interface

FCS = Future Combat Systems

AAR = After Action Review

Figure 2. Use Case 2, showing activities of the commander and other unit members during 

conduct of a collective training exercise. 

For each of these decision points, further analysis was needed to determine how the user 

would make those decisions—what information would be available, what options would be 

offered, and which decisions could have meaningful defaults. Additionally, the use case glosses 

over such necessary activities as scheduling, obtaining resources, notifying and assisting 

participants, and constructing the TSP. The secondary use case was intended to explicate those 

activities in terms of the performer—the TMS(C) or a human exercise controller. 

Interestingly, what Use Case 2 showed us was that the unit members’ actions during the 

exercise were completely focused on reacting to scenario conditions and events and performing 

their tasks. The description of their activities reads very like a description of actual combat 

19 
operations. Having all of the scenario conditions and enemy and other friendly unit actions 

provided from outside the unit is the norm in large–scale exercises. In today’s collective 

exercises, the commander might be consulted on whether to adjust the exercise in mid-stream in 

response to the unit’s ability to benefit from the experience, but this is not usually the case. For 

Use Case 2, further analysis was needed to determine whether the control exerted from outside 

the unit would, or should, come from a human exercise controller, CGF operator, or O/C, or 

whether, alternatively, it would come from the TMS(C). 

Findings—Secondary Use Cases 

Extending the process used to explicate the primary use cases, we prepared secondary use 

cases that focused on the person(s) or system(s) that support the commander, training manager, 

or unit. To the extent that the support comes from humans, there will be a need for tertiary use 

cases that expand on what those humans need from the TMS(C). As will be shown below, there 

was rarely a need for human participation, given the mature technologies described earlier. The 

only real exceptions occurred if, for whatever reason, the commander were to elect to have a live 

O/C, and in the case of live FCS exercises, where an exercise controller would direct the 

activities of live roleplayers. 

Figure 3 shows the secondary analysis for Use Case 1, development of and preparation for 

the training event. In the diagrams, the strand on the right represents actions of the user, which is 

essentially the same as what was presented in Figure 1. The left strand shows what the TMS(C) 

provides to cue the user or to accept the user’s response. 

In Use Case 1, much of the support for the user is in the form of data that have been 

analyzed and formatted to satisfy the user’s need for information to support decision-making. In 

describing where those data come from, we make reference to information available from the 

database. In fact, the database will be a number of databases, such as those described earlier, 

that are linked—that is, able to share information using integrated routines. It will comprise 

multiple data tables or datasets, elegantly structured so as to minimize redundancy and optimize 

processing speed. To the user, however, it will truly appear to be one massive set of data. We

made the assumption that, because the WMI, the TMS(C), and a host of other interfaces and 

databases will be completely networked, each of these will be able to export and import data 

from the others, seamlessly, using integrated routines that provide usable, readable reports or 

data tables. The interfaces, wizards, and help functions in those systems will be sufficiently 

well-developed that specialized technicians will not be required to manipulate bits and bytes to 

enable viewing of usable information. 

20 
...prepares for the event, individually and with unit

members, taking advantage of access to additional

material via the reach capability through WMI (M)

...views recommended training events (A)

...selects the type of training event (individual, 

collective, echelon, mission) (B)

...reviews options for pre-training (C)

...decides whether to schedule or dismiss the

suggested training (D)

...reviews recommended training venue(s) and

training date (E)

...selects a venue and date (F) 

...reviews options for exercise parameters (G)

...examines a representation of the exercise and 

TSP summary (J)

...requests changes to reflect his intent (or approves

in toto) (K)

...gives the instruction to distribute the readahead

package (individual and unit preparation) (L)

...gives the instruction to notify participants and

arrange for resources (I)

...selects parameters values/accepts defaults (H)

The User...

...current readiness status for the unit and key

personnel on METL tasks and supporting

individual tasks, as well as upcoming scheduled 

training activities for the unit and higher echelons

...assessment of unit and individual readiness for 

the selected training event based on training,

turbulence, individual assignments,

recommendations on pre-training based on task

decay rates

...recommendation on venue (stationary FCS, 

NRFTT, moving FCS, PC–type) and training

date(s) (based on preparation time needed and 

other events in the unit’s calendar)

...recommendations on appropriate terrain, enemy

type, and capabilities matched to probable 

deployment, duration time for training event, 

intensity or difficulty level of the conditions and 

scenario events, environmental conditions

(day/night, weather), level of unit participation (all 

or some), level of automated assistance

...representation of the exercise in four dimensions

(visual 3D plus time) that also indicates the task

steps, and a summary of the TSP

TMS(C) provides...

...overview of TSP contents and preparation steps

...links to reach-accessed information, preparation 

guidance

Note: Acronyms for Figure 3.

TMS(C) = Training Management System (Concept) PC = personal computer

METL = Mission Essential Task List TSP = Training Support Package

FCS = Future Combat Systems WMI = Warfighter Machine Interface

NRFTT = Networked Reconfigurable Full Task Trainers

...prepares for the event, individually and with unit

members, taking advantage of access to additional

material via the reach capability through WMI (M)

...views recommended training events (A)

...selects the type of training event (individual, 

collective, echelon, mission) (B)

...reviews options for pre-training (C)

...decides whether to schedule or dismiss the

suggested training (D)

...reviews recommended training venue(s) and

training date (E)

...selects a venue and date (F) 

...reviews options for exercise parameters (G)

...examines a representation of the exercise and 

TSP summary (J)

...requests changes to reflect his intent (or approves

in toto) (K)

...gives the instruction to distribute the readahead

package (individual and unit preparation) (L)

...gives the instruction to notify participants and

arrange for resources (I)

...selects parameters values/accepts defaults (H)

The User...

...current readiness status for the unit and key

personnel on METL tasks and supporting

individual tasks, as well as upcoming scheduled 

training activities for the unit and higher echelons

...assessment of unit and individual readiness for 

the selected training event based on training,

turbulence, individual assignments,

recommendations on pre-training based on task

decay rates

...recommendation on venue (stationary FCS, 

NRFTT, moving FCS, PC–type) and training

date(s) (based on preparation time needed and 

other events in the unit’s calendar)

...recommendations on appropriate terrain, enemy

type, and capabilities matched to probable 

deployment, duration time for training event, 

intensity or difficulty level of the conditions and 

scenario events, environmental conditions

(day/night, weather), level of unit participation (all 

or some), level of automated assistance

...representation of the exercise in four dimensions

(visual 3D plus time) that also indicates the task

steps, and a summary of the TSP

TMS(C) provides...

...overview of TSP contents and preparation steps

...links to reach-accessed information, preparation 

guidance

...prepares for the event, individually and with unit

members, taking advantage of access to additional

material via the reach capability through WMI (M)

...views recommended training events (A)

...selects the type of training event (individual, 

collective, echelon, mission) (B)

...reviews options for pre-training (C)

...decides whether to schedule or dismiss the

suggested training (D)

...reviews recommended training venue(s) and

training date (E)

...selects a venue and date (F) 

...reviews options for exercise parameters (G)

...examines a representation of the exercise and 

TSP summary (J)

...requests changes to reflect his intent (or approves

in toto) (K)

...gives the instruction to distribute the readahead

package (individual and unit preparation) (L)

...gives the instruction to notify participants and

arrange for resources (I)

...selects parameters values/accepts defaults (H)

The User...

...current readiness status for the unit and key

personnel on METL tasks and supporting

individual tasks, as well as upcoming scheduled 

training activities for the unit and higher echelons

...assessment of unit and individual readiness for 

the selected training event based on training,

turbulence, individual assignments,

recommendations on pre-training based on task

decay rates

...recommendation on venue (stationary FCS, 

NRFTT, moving FCS, PC–type) and training

date(s) (based on preparation time needed and 

other events in the unit’s calendar)

...recommendations on appropriate terrain, enemy

type, and capabilities matched to probable 

deployment, duration time for training event, 

intensity or difficulty level of the conditions and 

scenario events, environmental conditions

(day/night, weather), level of unit participation (all 

or some), level of automated assistance

...representation of the exercise in four dimensions

(visual 3D plus time) that also indicates the task

steps, and a summary of the TSP

TMS(C) provides...

...overview of TSP contents and preparation steps

...links to reach-accessed information, preparation 

guidance

Note: Acronyms for Figure 3.

TMS(C) = Training Management System (Concept) PC = personal computer

METL = Mission Essential Task List TSP = Training Support Package

FCS = Future Combat Systems WMI = Warfighter Machine Interface

NRFTT = Networked Reconfigurable Full Task Trainers

Figure 3. Secondary Use Case 1, showing information flow between the user and Training 

Management System (Concept) during development of an exercise. 

21 
The specific data needs derived from Use Case 1 are shown in Table 1, along with a 

generic indication of the specific type of database (or segment of a larger database) where the 

data would be found. In Table 1, the information needs are listed in order by source, so that the 

type of data from each source is obvious. All of the information needs and types of databases 

already exist (e.g., CATS, ATIS) except for the scenario repository. In the envisioned 

repository, the various components of TSPs will be stored as objects that can be searched, 

acquired, modified, and assembled into a logical and coherent TSP with no inconsistencies or 

loose ends. The TSP components will include those listed in TRADOC Regulation 350-70 (DA, 

1995) for collective TSPs, and the specific objects stored in each component table will be 

derived primarily from user-produced TSPs. Each object will have metatags or hooks that define 

the ways in which objects can be combined. For example, it would not be logical to have a 

platoon exercise in which the platoon is expected to engage the entire army of the enemy 

country, nor to have a brigade-size element sent out to take down a single sniper. 

Table 1 

Use Case 1 Information Needs and Sources 

Information Needed Source 

• Individual training plan, assignments and training history, and current readiness status 

for commander, staff, subordinate unit commanders .............................................................. Personnel database 

• Individual and staff readiness (task proficiency) for tasks in exercise..................................... Personnel database

• Unit-specific Combined Arms Training Strategy (CATS), Mission Essential Task List

(METL), point in life cycle, unit proficiency from previous exercises, unit’s scheduled 

deployments, unit’s personnel turbulence history ................................................................... Unit database 

• Scheduled training from unit training calendar ....................................................................... Unit database 

• Available window(s) in training calendar with sufficient time for preparation ....................... Unit database 

• Geographic location of next likely deployment ....................................................................... Unit database 

• Intensity level appropriate to current proficiency .................................................................... Unit database 

• Unit naming conventions and call signs................................................................................... Unit database 

• Recommended training frequency for each unit METL task .................................................. Task database 

• Prerequisite individual and collective tasks for each task in exercise...................................... Task database

• Recommended venue(s) for tasks in exercise .......................................................................... Task database

• Time required to train to proficiency from current level ......................................................... Task database 

• Exercise specifications for initialization .................................................................................. Task database 

• Task performance measures, standards, and steps for after action review (AAR) template .... Task database 

• Scenario events that will cue task performance or allow for task observation; include in

AAR template .......................................................................................................................... Task database 

• Training resources by venue .................................................................................................... Venue database

• Options for environmental conditions for venue...................................................................... Venue database

• Mission appropriate to unit type and selected tasks................................................................. Scenario repository 

• Exercise components that match exercise specifications......................................................... Scenario repository 

• Segment of selected terrain appropriate for mission ................................................................ Scenario repository 

22 
In every case, the accessed information would be packaged so that it is presented to the 

user in a form that he can use. In some cases, that packaging will use pre–formatted report or 

table templates, supported by analytic routines that compare or combine different bits of 

information. For example, in determining the unit’s readiness for an exercise, the information on 

individual proficiency would be estimated (calculated using a developed algorithm) from records 

of previous training and recent assignments; the results would be used to look up appropriate 

training opportunities to satisfy the need; the information on all key staff and subordinate unit 

commanders would be packaged using a table template; and the table would be presented to the 

user to show critical training prerequisites by individual. 

In other cases, the presentation will be in the form of pick-lists, which will show options 

for the commander to select. For example, the commander will be asked to decide on the terrain 

for the exercise, and will have the option of selecting the one most appropriate to his upcoming 

deployment, or one that allows him to have the unit practice those skills that he deems critical. 

The template and option pick list needs identified by means of the secondary analysis of 

Use Case 1 are shown in Table 2. Review of the items in the list makes it fairly obvious that 

presenting the information will not be especially difficult—even a COTS product like MS® 

Access® has tools for reporting database information. Rather, creating the algorithms and 

analytic routines to transform the raw data items shown in Table 1 and the selected options in 

Table 2 into information for the reports will be the challenge.

Table 2 

Use Case 1 Information Provided to User 

Information to be Provided Form 

• Recommended options for unit training, in terms of mission, tasks, terrain, intensity ............................ Option list

• Recommended scheduling and venue(s) options, with description of preparation strategy and

resource requirements for venue(s).......................................................................................................... Option list 

• Options for environmental conditions for venue ..................................................................................... Option list

• Options for participation (subordinate units, commanders, and staff); level of coaching (unobtrusive, 

one-on-one, pause and coach); live Observer/Controller (O/C) or automated observation, feedback, 

and after action review (AAR); and adjustment of exercise intensity based on unit performance........... Option list 

• Report on individual and unit training status, readiness to participate in exercise, prerequisite 

training needs, criticality of each prerequisite, and recommended training strategy ............................... Template 

• AAR plan showing training objectives, standards, and general discussion points................................... Template

• Exercise overview comprising situation summary, training objectives and standards, overview of 

exercise events, terrain sketch, and control measures.............................................................................. Template 

• Observation and AAR guidance for live O/C (as needed) ....................................................................... Template 

• Exercise specifications for networked reconfigurable full task trainers (NRFTT) facility manager 

(as needed) .............................................................................................................................................. Template 

Finally, for the commander’s review of the constructed exercise, the information will be 

packaged and shown in the form of a 4–dimensional representation (space plus time) that will be 

labeled with reference to the tasks that are targeted in the exercise. This presentation will have 

options for the commander to freeze the action, point to particular features, and request changes. 

23 
For example, in a platoon exercise, the tools that construct the TSP may have placed a concertina 

obstacle on a road, and the commander would rather the platoon encounter a suspected IED. 

In addition to the needs to find data and present information, there will also be a need to 

send information directly to other users or systems: exercise dates to the unit training calendar, 

resource requests to appropriate suppliers, and facility reservations (e.g., NRFTT) to the facility 

calendar or manager. These are functions that are already available in existing training 

management systems, such as DTMS. It should be noted that there is no need to send 

initialization data for the exercise to another system, as the development of those data and the 

use of the data are both functions of the TMS(C). 

The nature of the interaction between the TMS(C) and the unit members during conduct of 

the exercise, as shown in Figure 4, is quite different from what occurs during development 

(Figure 3). The TMS(C) will present almost nothing in the form of reports or option lists. 

During the exercise, much of the information will be in the form of simulated activity to 

represent the behaviors of enemy and other friendly units, physical effects on battlefield objects, 

environmental conditions, and the like (shown as Activities N–Q in Figure 4). This will be the 

case whether the exercise unfolds in accordance with the developed TSP, or whether the TMS(C) 

adjusts it based on the unit’s demonstrated proficiency and need for more (or less) challenging 

conditions (Activity T). It will also be the case when the TMS(C) needs to simulate the activity 

of any missing participants, whether that need was identified during development (Figure 3, 

Activity G) or occurs because an individual is pulled off-line for remediation (Activity S). 

The simulated activity will be presented by means of dynamic, physics-based modeling of 

the entities in the simulated environment. Physics-based modeling allows developers to define 

the structure and behavioral characteristics of entities (e.g., vehicles, units, physical objects, 

nonparticipating unit members), in terms of how they move or act and how they react. In the 

case of physical entities, other modeling will include how they blow apart or collapse when 

destroyed, how vulnerable they are to different hits (which are also modeled with 

characteristics), and so on. For the characteristics of human simulations, the modeling will be 

focused on how they perform their tasks in reaction to a variety of stimuli that, together, appear 

to define unpredictable situations. These behavioral rule sets will also have defined levels of

stochastic reactions to allow for the imperfections in modeling. This will permit the TMS(C) to 

control the scenario events at a fairly high level, by releasing general guidance concerning events 

and timing and allowing the entities to operate according to their rule sets within that guidance. 

Adjusting the difficulty level of the exercise will be done in one of two ways. As the 

TMS(C) compares unit performance to standards and determines that the unit needs more (or 

less) of a challenge, it will search the scenario repository for appropriate TSP components to 

present the appropriate change. If the TMS(C) locates likely TSP components, it will test them

for realism and consistency with the portions of the exercise already completed, modify as 

needed to match the difficulty level, conditions, and objectives for the exercise, and swap them

into its operating plan to replace the existing components. If appropriate TSP components do not 

exist in the scenario repository from previous training events (see the final TMS(C) activity in 

Figure 4), the TMS(C) will construct those components from an underlying physics-based model 

of TSP component elements. 

24 
...receive guidance on additional training and 

exercises to reinforce skills (W)

Unit members...

...see and hear scenario conditions and cues 

through WMI and other FCS channels (N)

...see and hear indicators of enemy and other

friendly unit reactions to unit’s actions (O)

...see and hear physical effects of actions (P)

...use FCS to perform mission tasks (Q)

...receive individual and unit

coaching (R)

...receive one-on-one remedial

instruction (S)

...experience adapted exercise (T)

...experience pause in exercise with 

collective remedial instruction (U)

...participate in AAR discussions (V)

...receive feedback reports on their individual

task performance (X)

TMS(C) provides...

...initialization of CGF, realistic scenario conditions and

cues based on MEL and timeline in TSP, dynamic terrain

...intelligent behaviors of enemy and other friendly units

based on physics-based behavioral definitions

...dynamic representation of effect based on physics

based models of objects

...emulation of FCS controls and interfaces that are not in

operational mode, emulation of performance support 

systems and reach capability

...collection of

performance data, 

assessment

according to

performance

standards, 

identification of

correct action

...remedial instruction for individual, 

simulation of the individual in the 

exercise until the individual returns (for

complex but isolated problems)

...more difficult or less difficult 

conditions and situation (to match unit

readiness to benefit from the exercise)

...pause in exercise, remedial training, 

remedial exercise (for serious 

widespread problems)

...4D recording of exercise annotated with assessment of

performance on tasks, discussion points, option for 

commander to add comments or customize AAR

...recording/transcription of AAR discussion with “sustain” 

and “improve”, recommendations on additional training

...reports on individual performance tied to exercise

tasks, archiving of exercise summary and performance 

assessment

...cues and comments to individuals (for

simple problems)

...assessment of training event utility, refinements based 

on conduct of exercise, archiving of TSP elements

Note: Acronyms for Figure 4.

TMS(C) = Training Management System (Concept) WMI = Warfighter Machine Interface

CGF = Computer-Generated Forces FCS = Future Combat Systems

MEL = Mission Event List AAR = After Action Review

TSP = Training Support Package

...receive guidance on additional training and 

exercises to reinforce skills (W)

Unit members...

...see and hear scenario conditions and cues 

through WMI and other FCS channels (N)

...see and hear indicators of enemy and other

friendly unit reactions to unit’s actions (O)

...see and hear physical effects of actions (P)

...use FCS to perform mission tasks (Q)

...receive individual and unit

coaching (R)

...receive one-on-one remedial

instruction (S)

...experience adapted exercise (T)

...experience pause in exercise with 

collective remedial instruction (U)

...participate in AAR discussions (V)

...receive feedback reports on their individual

task performance (X)

TMS(C) provides...

...initialization of CGF, realistic scenario conditions and

cues based on MEL and timeline in TSP, dynamic terrain

...intelligent behaviors of enemy and other friendly units

based on physics-based behavioral definitions

...dynamic representation of effect based on physics

based models of objects

...emulation of FCS controls and interfaces that are not in

operational mode, emulation of performance support 

systems and reach capability

...collection of

performance data, 

assessment

according to

performance

standards, 

identification of

correct action

...remedial instruction for individual, 

simulation of the individual in the 

exercise until the individual returns (for

complex but isolated problems)

...more difficult or less difficult 

conditions and situation (to match unit

readiness to benefit from the exercise)

...pause in exercise, remedial training, 

remedial exercise (for serious 

widespread problems)

...4D recording of exercise annotated with assessment of

performance on tasks, discussion points, option for 

commander to add comments or customize AAR

...recording/transcription of AAR discussion with “sustain” 

and “improve”, recommendations on additional training

...reports on individual performance tied to exercise

tasks, archiving of exercise summary and performance 

assessment

...cues and comments to individuals (for

simple problems)

...assessment of training event utility, refinements based 

on conduct of exercise, archiving of TSP elements

...receive guidance on additional training and 

exercises to reinforce skills (W)

Unit members...

...see and hear scenario conditions and cues 

through WMI and other FCS channels (N)

...see and hear indicators of enemy and other

friendly unit reactions to unit’s actions (O)

...see and hear physical effects of actions (P)

...use FCS to perform mission tasks (Q)

...receive individual and unit

coaching (R)

...receive one-on-one remedial

instruction (S)

...experience adapted exercise (T)

...experience pause in exercise with 

collective remedial instruction (U)

...participate in AAR discussions (V)

...receive feedback reports on their individual

task performance (X)

TMS(C) provides...

...initialization of CGF, realistic scenario conditions and

cues based on MEL and timeline in TSP, dynamic terrain

...intelligent behaviors of enemy and other friendly units

based on physics-based behavioral definitions

...dynamic representation of effect based on physics

based models of objects

...emulation of FCS controls and interfaces that are not in

operational mode, emulation of performance support 

systems and reach capability

...collection of

performance data, 

assessment

according to

performance

standards, 

identification of

correct action

...remedial instruction for individual, 

simulation of the individual in the 

exercise until the individual returns (for

complex but isolated problems)

...more difficult or less difficult 

conditions and situation (to match unit

readiness to benefit from the exercise)

...pause in exercise, remedial training, 

remedial exercise (for serious 

widespread problems)

...4D recording of exercise annotated with assessment of

performance on tasks, discussion points, option for 

commander to add comments or customize AAR

...recording/transcription of AAR discussion with “sustain” 

and “improve”, recommendations on additional training

...reports on individual performance tied to exercise

tasks, archiving of exercise summary and performance 

assessment

...cues and comments to individuals (for

simple problems)

...assessment of training event utility, refinements based 

on conduct of exercise, archiving of TSP elements

Note: Acronyms for Figure 4.

TMS(C) = Training Management System (Concept) WMI = Warfighter Machine Interface

CGF = Computer-Generated Forces FCS = Future Combat Systems

MEL = Mission Event List AAR = After Action Review

TSP = Training Support Package

Figure 4. Secondary Use Case 2, showing Training Management System (Concept) activity 

during conduct of an exercise. 

25 
When there is a need for unobtrusive coaching, the nature of the interaction will shift 

slightly as the TMS(C) presents leading questions or probes that cue the individual unit member 

to consider particular information or perform particular actions (Activity R). Providing more 

direct remediation to individuals (Activity S) or activating a pause with directive instruction 

(Activity U) will put the TMS(C) in the anthropomorphic role of a trainer, giving information 

and asking questions rather than presenting cues. The AAR will also be conducted by the 

TMS(C), which will play selected segments of the recorded exercise in 4-dimensional format (3-

dimensional space plus time), present discussion points, probe for additional analysis, and 

capture information on identified strengths and weaknesses. 

As with development and preparation for the exercise (Use Case 1), there will be relatively 

little need for live trainers or controllers. If there is to be a live O/C (selected by the user during 

development in Activity G), the TMS(C) will provide preparation guidance and in-process 

guidance for coaching and feedback, and will support the O/C’s situational awareness by 

allowing him to view the exercise from any vantage point and to access collected data about the 

unit’s performance. It will also assist the O/C in preparing the AAR, conducting the AAR 

discussion, capturing the outcomes, and preparing the report. Discussions of O/C location, either 

with the unit or remote, will be moot: the capability for distributed training will apply not only 

to the unit members but also to any observers. 

Similarly, even in the case of “live” FCS-based training, where the users are moving their 

vehicles across real terrain and viewing a combination of real objects (terrain features and each 

other) and virtual objects (enemy and other friendly vehicles, weapons effects) by means of 

augmented reality, there will be only a minimal need for a human exercise controller. The 

TMS(C) will be able to track activities of the live FCS vehicles, and control the reactions and 

appearance of simulated objects. Whether or not we will entrust safety considerations and 

control to the TMS(C), or require the vigilance of a live exercise controller, remains to be seen. 

If there is a human controller, which there would be if live roleplayers were used (for civilians or 

enemy troops, for example), the TMS(C) will provide streaming guidance to the human 

controller on directing their activities. This could even be done when the TMS(C) adjusts the 

exercise intensity. 

There are two archiving activities shown in Figure 4, both of which will occur at the 

conclusion of the exercise. The first concerns recording the exercise outcomes. The TMS(C) 

will communicate directly with the unit database to record information on unit task proficiency, 

identified strengths and weaknesses, and recommended next training. Information on individual 

task proficiency will be derived from the exercise records by use of algorithmic routines, and 

recorded in the personnel database. These results will then be available to the user when he next 

plans a training event. Note that no roll-up reports that use the separate data points will need to 

be stored, as integrated routines can compile the reports as needed. (This relates to the issue of

database normalcy.) 

The other type of archiving concerns preservation of the TSP for future use. After an 

exercise is completed, the TMS(C) will make an assessment of the utility of the training event, 

based on unit performance improvement, robustness of AAR discussions, and general coherence 

of the TSP components. The commander or training manager will also be asked if the training 

26 
event was useful for the unit. For training that is judged as effective and efficient, the TMS(C) 

will parse the TSP back into its component pieces, update the metadata for pieces that were 

modified, and archive the new, revised, and unrevised components to the scenario repository. 

The Issues and the TMS(C) 

The expanded analysis of the two use cases, together with examination of the work already 

being done on training management for current and Future Forces, allowed us to identify and 

isolate a number of needs that are already being addressed, as well as some that are not. Recall 

the five issues and the main discussion points: 

• What will future training development and delivery be like? Discussion of FCS 

planning for software services and training roles was presented. 

• What kind of information and tools will the user need? Discussion focused on two 

situations—unit leaders planning collective training and the units participating in 

adaptable collective training. 

• How will the information and tools be provided (or obtained, or produced)? Four 

current and emerging Army database initiatives were discussed, as well as a brief

review of CGF and AI technologies. 

• What kind of tools (i.e., how much automation) will training managers accept? The 

need for commanders to place a personal imprint on exercises rather than allow a 

TMS(C) to make decisions and act as the expert was discussed. 

• What are the near-term solutions? Discussion pertaining to this issue was deferred to

this section.

The picture of future training development and delivery in the use cases is, we think, 

comprehensible and consistent. However, that picture and the TMS(C) it describes rely heavily 

on future technology capabilities for the information and tools that the user will need: a complex 

database system, bi-directional reach, sophisticated self-learning search engines, performance 

support systems, CGF–simulation of conditions and participants powered by AI, and an 

upgraded bandwidth. 

The future in which that picture can be a reality is a long way off, probably beyond the 

2016-2020 timeframe when the first units will be fully equipped with FCS. Fortunately, while 

the capabilities are critical for the use cases’ picture, they are not the sine qua non for good 

training to be available to Future Forces by 2016. 

This brings us squarely to the third issue: How will the information and tools be provided? 

If the TMS(C) depends on technologies that will not be available, then the picture of future 

training changes. Using the use case explications and our best estimate of the maturity of those 

capabilities, we formulated interim solutions, as follows (and as summarized in Table 3). 

27 
Table 3 

Summary of Capability Needs and Research and Development Needs 

Capability Need Reality

Research and 

Development Needs 

Databases –

personnel,

unit, task, 

venue 

Easily accessible, with standards and 

specifications for interfacing with each 

other; integrated reports and other tools 

that will make them easy to use. 

Already underway (Army 

Training Information

System [ATIS], Defense 

Integrated Military Human

Resources System

[DIMHRS]).

Research on data needs, 

report needs, ways of

compiling and analyzing 

data to produce usable 

results. 

Scenario 

repository

and registry

Tools for developing and archiving

multiple versions of each training support 

package (TSP) component, finding

versions of the components with specific 

characteristics, and combining selected 

versions of the components to build a 

TSP; capability to modify components, 

have modifications cascade through the 

assembled TSP and effect related 

modifications, and check the finished TSP 

product for internal consistency. 

Possibility that TSP 

modification tools can be 

developed. Unlikely that

automated TSP 

construction tools will be

developed. 

Repositories and

registration of whole TSPs 

or large–granularity TSP 

components that can be 

modified as needed, based 

on the six Future Combat

Systems (FCS) scenarios.

 

Identification of TSP 

components and possible 

combinatory rules. 

Bi-directional 

reach 

Overarching network that will connect all 

FCS to each other and to their homestation operations center (HSOC), to

support direct point-to-point contact;

provide access to databases and 

repositories, remote observer/controllers

(O/Cs), additional information about 

tactical conditions, and additional TSPs. 

Highly likely; network is at 

the heart of FCS. 

Delineation of specific 

information requirements. 

Search

engines 

Capability that indexes available content

and performs intelligent searches; able to

“learn” and replicate quickest paths and 

most usable content. 

Likely; still evolving with

no end in sight. 

Investigation of most useful

search engine capabilities 

and display modes. 

Performance 

support

systems, 

future 

computergenerated

forces (CGF) 

and AI 

Multiple tools (e.g., automated cognitive 

decision and planning aids, 

communication and service support aids, 

collaborative problem solving aids, and

tools for terrain and automated pattern

analysis). 

Unlikely; processes will 

resemble the current 

processes, with human 

training managers, exercise 

controllers, and CGF 

operators; performance 

support systems without 

CGF and AI are likely.

Automated networkenabled tools to support 

training manager, exercise

controller, and CGF 

operator. 

Bandwidth Increased bandwidth for real-time 

presentation of synchronous simulations 

to multiple distributed participants, with

ongoing data collection and performance 

evaluation, options for observation, and 

dynamic automated adjustments; reliable 

security measures and multiple

redundancies. 

Highly likely; network is 

essential for FCS, making 

bandwidth solutions a 

central requirement. 

Research compression 

techniques and effect on 

data transmitted; develop

techniques and best 

practices for workarounds.

28 
Database Systems 

Given the progress that has already been made with CATS and with early versions of 

DTMS and ATIS, it is likely that another 10 years will be sufficient to mature the TDDT and 

DIMHRS. Our guess is that the databases listed in Table 1 for personnel, unit, task, and venue 

information will be accessible and will have standards and specifications for interfacing with 

each other. Furthermore, they will have integrated reports and other tools that will make them

easy to use for normal people (e.g., commanders, training managers). From a technology and 

human effort point of view, there is no reason that the databases should not be operational. 

Considerations of data security are integral to personnel database development and use, and are 

expected to continue to be addressed. At the same time, it will be important to provide similar 

safeguards on any systems that use those databases, such as a future training management 

system. 

The scenario repository referred to in Table 1 and Use Case 1 is another matter, somewhat 

further from maturity. It is, in fact, more than a repository. The need is for a way to develop and 

archive multiple versions of each TSP component, find versions of the components with specific 

characteristics, and combine selected versions of the components to build a TSP. Furthermore, it 

must be possible for an intelligent system such as the TMS(C) to modify components, to have 

those modifications cascade through the assembled TSP and effect related modifications, and to 

check the finished TSP product for internal consistency (more on the required intelligence 

below). 

If anyone has identified ways to define the specifications for TSP component objects, 

constructed the objects, or built a repository and registry for those objects, they are being fairly 

secretive about it. Repositories and registration of TSP component objects may be the bridge too 

far for 2016. If we can’t have the capabilities for TSP component objects, then the next best 

thing would be repositories and registration of whole TSPs that can be modified as needed. (The 

modification capability, related to the AI requirement, is described below.) The scenarios and 

tactical situations around which the first base set of TSPs should be constructed can be derived 

from the six scenarios that are widely used in the current FCS analytic work (Unit of Action 

Maneuver Battle Laboratory [UAMBL], 2004). 

This may be a realistic goal for 2016. Work being done on the Content Object Repository 

Data Registration Architecture (CORDRA) is exploring ways to register learning content 

developed according to Shareable Content Object Reference Model (SCORM) specifications, 

and to parse larger sets of content into separate objects by means of the object metadata (Dodds, 

2004). Additionally, the Institute of Electrical & Electronics Engineers (IEEE) Learning 

Technology Standards Committee has formed a standards study group to examine the 

possibilities for interfacing instructional systems and simulation systems (G. Franks, personal 

communication). These two initiatives may form the foundation for work on constructing a 

collective exercise version of the SCORM work. Given current and recent efforts, we would 

anticipate that some versions of such a system of repository and registry, the specifications and 

standards, and the TSP content to populate the system may be operational by 2016. In fact, once 

the repository and registry are created, the content may be registered by parsing constructed 

TSPs into component objects at a fairly high level of granularity, as is done with SCORM–

29 
conformant content under the CORDRA specifications. It seems likely that the technology 

would allow a working system, if individuals and agencies are inclined to work on it. 

Bi–Directional Reach 

This is such an integral requirement for the FCS that we have every confidence (tempered 

by pessimism born of experience with technology initiatives) that the necessary capabilities will 

be available. The overarching network that will connect all FCS systems, not only to each other 

but also to their HSOC, will support direct point-to-point contact, eliminating relays of

information. For training management specifically, commanders will have access to the 

databases and repositories described above, to remote O/Cs, to additional information about the 

tactical conditions underlying the exercise, and to individual and small group training that can be 

used in preparation for the exercise.

It seems unnecessary to describe an interim solution for 2016. If the capability is not 

operational, it will likely be a problem with interfaces among databases and repositories, 

adaptable search engines, or bandwidth (described below), rather than any inherent difficulty in 

enabling communications. 

Search Engines 

Powerful search engines are needed to support both the reach capability and the TSPbuilding process. The user cannot be expected to sift through all of the available information 

that contains his chosen keyword. Additionally, trying to organize information so that the user 

can look through all available information will be impossible, given the multiple users and their 

multiple needs. Rather, the need is for a powerful Google-like search capability (sort of a 

Googleplex) that indexes available content and performs intelligent searches. Each time a search 

is performed, the system “learns” the quickest path and most usable content. Wall, Elms, 

Biggers, and Sticha (2004) developed a prototype knowledge network that looked at ways to 

organize information, find the most relevant information for a user’s request, authenticate the 

information, and allow users to add information to the network. This type of system will be

needed to facilitate the reach capability. 

The Defense Advanced Research Projects Agency (DARPA), which invented the internet, 

is moving far beyond Google in the development of advanced search engines. For example, a 

not-so-recent DARPA-sponsored project at Insightful Corporation led to the development of a 

product known as InFact, based on linguistic normalization and making use of sophisticated 

technology for modeling the morphology, semantics, and syntax of languages (DARPA, 2003). 

It captures a larger amount of information than other search engines and can “read” documents 

while retaining the information content of every sentence. 

Rather than retrieving information by keywords, as did early search engines, or by 

concepts and patterns, as did the next generation, the third generation of search engines will 

retrieve facts. The emerging technologies use language models that examine morphology for the 

analysis of word form, syntax for the analysis of keyword relationships, and semantics for the 

analysis of overall linguistic meaning. Morphological and semantic analyses are not new to 

30 
search and retrieval, but it is the inclusion of the syntactic model that will allow search engines to 

understand meanings and duplicate the process of lexical comprehension in humans. 

Furthermore, many of the next generation search engines have unique ways of grouping and 

displaying results (e.g., concept maps, concept spaces, semantic webs and visualization, and 

semantic indexing) that make it easier to find relevant information. They also have the ability to 

use contextual and positional operators, query by example, query expansion using related words 

and concepts, improved faceting, and data mining technologies to improve search and retrieval 

results (S. Shadrick, personal communication).

In the very near-term, Google already exists. Further developing the process and turning 

the ’bots loose on the developed databases and repositories should not be an obstacle. If nothing 

else, we will have more primitive ways of searching and accessing information, which means 

things will take longer, users will be discouraged by the laborious routines involved in finding 

what they need, and they will stop searching. This capability has both a high likelihood of 

success and a dearth of interim solutions. 

Performance Support Systems, Computer-Generated Forces Simulation, and Artificial 

Intelligence

This discussion combines the topics of performance support systems with considerations of 

future CGF and AI because of the nature of those future performance systems, as described in 

FCS documents. Many of the most useful of the planned systems for FCS will be enabled by 

powerful CGF and AI, so that the technologies and products are inextricably linked. 

The FCS program developers have a strong commitment to performance support 

systems—that is, ways of presenting the appropriate information, at the appropriate time and 

level of specificity, using techniques appropriate to the user’s ability and needs. Plans for FCS 

capabilities are rife with references to various software services. The need for leaders and their 

staffs, as well as system operators, to use the same tools during training that they use in 

operations is indisputable. These same tools, however, are also needed for the TMS(C) 

features.10

The distinction between training support systems and performance support systems is 

moot. If the performance support systems are available via the SOSCOE in the platforms, and 

embedded training is available in the platforms via the SOSCOE, then there is no reason that 

they should not take advantage of the same technology—it would be foolish to do otherwise. 

The tools and software services are of limited utility without AI, and many also require 

CGF. As described earlier, the related fields of CGF simulation and AI have a long way to go, 

but also have devoted researchers. The adaptive training system requires both CGF and AI for 

complete development of the performance support systems, in order to dynamically present 

conditions reactive to unit actions, evaluate unit member performance against competency 

definitions and determine needs for coaching and remediation, present space and time models for 

 

10 Descriptions of the specific software services has not been designated for open distribution. Interested readers 

should contact the LSI for more information. 

31 
scenario development and AARs, construct internally consistent training events and TSPs, lead 

AAR discussions and recording results, and so on. 

Engineers and programmers are already at work on those systems and technologies, but the 

probability of success by 2016 is moderate at best (in our opinion), despite some very dedicated 

efforts. Even if the technologies are sufficiently matured to permit the tools to be deployed, 

there is a distinct possibility that they will not be assembled into the training support tools 

posited for the TMS(C). 

If the various performance support tools are not operational (with their supporting CGF 

and AI capabilities), then processes for training development and execution will resemble the 

current processes, where individual training managers, exercise controllers, and CGF operators 

make decisions and carry them out with live roleplaying participants and only the barest of 

automated tools.11 All three of the training management roles will need to be filled by humans, 

rather than by computerized services. Roleplayers and O/Cs will require training and their own 

performance support, just as they do today. The TSPs may not need to be predominantly paper, 

but they will need human translation to the simulator devices, while an instantiated TMS(C) 

would actually serve as the TSP for many purposes currently performed by humans with paperor computer-delivered guidance (e.g., providing standardized controlled conditions, providing 

support for feedback). 

If the mechanisms for automatic and intelligent construction of training events and TSPs 

are not developed—then users should at the very least have semi–automated tools such as the 

Commander’s Integrated Training Tool (CITT), developed by ARI for the Close Combat 

Tactical Trainer (CCTT; Flynn et al., 2001). That tool set led the user through a process to 

identify an appropriate training event and download the matching TSP. The user could then 

modify the TSP to adjust starting positions and unit names and call signs, and produce an 

exercise initialization file for CCTT, as well as the other paper-based materials that would be 

used to guide unit preparation, O/C activities, control of CGF during the exercise (by humans), 

and AARs. If no TSP was found that was close to the identified training need, then the user was 

instructed in how to develop the exercise specifications and the TSP. 

More recent work at ARI is examining rapid scenario generation (A. Cianciolo, personal 

communication), although the prototype tools are still only semi–automated, largely requiring 

human development. The Joint Advanced Distributed Learning (JADL) Collaborative 

Laboratory (CoLab) is also pursuing the development of tools to assist with development of 

training support materials (JADL CoLab, 2005). Thus, there is some small flurry of interest in 

making the tools available. With continued interest and effort at the current pace, it should be 

possible to achieve the goal of developing the scenario repository and the tools that will support 

at least a CITT–like capability. 

 

11 These “barely automated” tools include, of course, semi-automated forces, simulated plan view and out-thewindow displays, templated reports and support materials, exercise initialization capabilities, and other technology

advances that were state-of-the-art only 10 years ago.

32 
Improved Bandwidth Capabilities 

The real-time presentation of synchronous simulations to multiple distributed participants, 

with ongoing data collection and performance evaluation, options for observation, and dynamic 

automated adjustments, will put a severe strain on a network with conventional bandwidth. The 

requirements are not only for increased bandwidth, but also for reliable security measures and 

multiple redundancies. 

These are not new thoughts—FCS developers and other researchers in and around DoD are 

well aware that they will need improvements just for operational purposes—but may increase the 

scope of the requirement. A presentation at a recent DARPA Tech (Saleh, 2005), described 

some of the ongoing DARPA research and plans concerning bandwidth issues. The DoD Global 

Information Grid (GIG) operates on the backbone of the Global DoD Enterprise Network, 

consisting of satellite-based networks, optical core and edge networks, and land-based fixed 

wireless networks, all with strong connectivity to tactical wireless networks. 

Currently, the optical core of the DoD Enterprise Network is the GIG Bandwidth 

Expansion (GIG-BE), now managed by the Defense Information Systems Agency (DISA). It is 

a state-of-the-art, fiber-optic network with the impressive projected aggregate capacity of 10 

terabits per second. However, to enable powerful network centric warfare applications, DARPA 

projects that the next-generation optical core network must have at least 10 times the capacity of 

GIG-BE (or an aggregate capacity of at least 100 terabits per second). According to DARPA, 

simply scaling up today’s technology to meet these enormously great demands is not a viable 

answer. If we actually try to do this, the mostly switching and routing nodes will become too 

expensive, too big, too power hungry, and far too hot. The solutions being explored by DARPA 

focus in part on creating the technologies for all-optical networking, dense wavelength-division 

multiplexing, and aggressive optoelectronic integration. 

For network-centric operations as envisioned in the FCS ORD, the next-generation GIG 

must be not only fast, but incredibly reliable, self-healing, and secure. If those requirements and 

the 100 terabits per second are achieved, and if training management features can share some of 

the future GIG capability, then it is likely that bandwidth will no longer be an issue. 

Based on description of efforts by DARPA (among others), we are cautiously optimistic 

that, in the next 10 years, the bandwidth capabilities will be orders of magnitude greater than 

they are today, at least for operations if not for training. The entire FCS concept is predicated on 

the notion of completely networked systems. Without the fully capable network, FCS itself is in 

peril, and embedded training in FCS is moot. 

Summary and Recommendations 

Our recommendations are based on a balance between what is most critical for future 

adaptive training, and what is most achievable by the 2016-2020 timeframe. They focus on the 

technologies and the ensuing capabilities (as opposed to the surrounding work involved in 

making them useful, described below), and target the efforts required of psychologists, military 

33 
experts, and instructional designers working in close collaboration with engineers and 

programmers. 

The work on the databases should include training experts who will be able to define and 

describe the variables of interest, and to delineate the connections among those variables. 

Separate databases or segments (tables) within databases should not contain redundant 

information or information that can be computed from existing data (again, the consideration of 

normalcy). Training experts should be able to generate the complete list of personnel, unit, task, 

and venue characteristics that are needed, draw the linkages and relationships, and tell why they 

are needed and how they will be used in clear and concise sentences. Then, working with 

database designers and engineers, the team will be able to design the structure of compatible 

databases that will share information with each other and with users. They will also be able to 

build the tools that will allow users to access appropriate reports and enable performance data 

archiving. This should be an effort accorded some high priority, primarily because the very 

promising ATIS and DIMHRS initiatives are already underway and should not be ignored. 

Additionally, work on scenario repositories and registries should expand to include 

definition of the scenario and TSP components, the ways in which they are related and can be 

defined as compatible or incompatible, interfaces and decision points for human input, and 

methods for relegating the compatibility decisions to an intelligent agent. Work should begin on 

codifying the process for translating the TSP components into coherent TSPs with information 

for human users and data bits used by the services of the embedded training TCCs. The scope of 

work on the scenario repository is very broad, and needs to be coordinated with work on the 

TCCs and BCSSs. We would also consider this to be a fairly high priority, not so much because 

of any urgency for quick solutions, but simply because it’s going to take some time. 

The bi-directional reach feature in support of both training and operations is already on the 

radar for FCS developers and engineers. The use of search engines to facilitate construction of 

training events, preparation for training, and obtaining information during training (as during 

operations) is going to take some thought. There are significant bodies of research literature on 

knowledge management, and the corpus expands daily. The role for training experts here is to 

maintain a watching brief on knowledge management within the military community and to be 

alert for ways to exploit emerging developments. With the passage of time, as decisions are 

made, training experts should become even more active in the process to ensure that those 

decisions are reflected in training management system developments. The priority to monitor the 

process is high, but the effort is of relatively low intensity. 

Continuing development of the FCS performance support systems, on the other hand, is 

going to be a fascinating area for research. It is still relatively embryonic, as a vast array of 

capabilities in CGF and AI are folded into the mix. The use of performance support systems 

should be largely human–centric, as opposed to technology–centric, designed to support human 

needs rather than to deliver every possible service. The involvement of experts in training, 

military requirements, human factors, and cognitive behaviors is essential to influence both the 

type of support provided and the ways in which it is provided. This area is considered as a high 

priority, both monitoring developments and participation in design and decision-making. 

34 
The likelihood for success by 2016 is not assured. It would be wise for training experts to 

continue to work on the interim solutions: less intelligent performance support systems for 

human training managers, exercise controllers, O/Cs, and CGF operators; CITT-like tools to 

facilitate construction of training events and TSPs from a core set based on the six scenarios 

(UAMBL, 2004); and templates that can be used with minimal human input supplementing 

database input to provide reports on training needs and accomplishment. The development of 

tools to permit rapid building of training events and TSPs is already being studied (ARI, 2005). 

The performance support systems for human training roles are of particular interest, and should 

be a fruitful area for research to determine the specific needs for each role, the ways of delivering 

the support, and the appropriate levels of automation. 

The work on bandwidth issues, including security and redundancy, is in the hands of 

engineers. Our only participation in these developments is to stay informed on the capabilities so 

that the proposed solutions do not render the training management system inoperable. For 

example, network security measures that prevented exchange of information between platforms 

during training would be counterproductive. Bandwidth allocation that did not permit distributed 

data collection, analysis, and AARs would seriously impede conduct of distributed exercises. 

There are other requirements for making the technologies and capabilities useful, such as 

populating the databases with personnel and unit training and experience information, 

documenting collective and leader task analyses, devising and recording the baseline CATS, 

creating the starter set for the scenario repository and registry, analyzing the expert, collective, 

and structural behaviors that CGF and AI will portray, preparing task standards that are 

measurable by intelligent systems, creating the individual training that can be used for 

remediation, among others. All of these will need attention, and findings will need to be 

integrated to permit exploitation. 

One issue stated earlier has been left hanging: What kind of tools (i.e., how much 

automation) will people accept? This is a field for research all by itself, and should probably get 

underway before all the tools are built. The early days of battlefield digitization were 

characterized by a tendency among leaders to revert to voice communications when stressed. 

The CITT, despite the obvious advantages it afforded in saving time and ensuring completeness, 

is not widely used today. In fact, a recent study of CCTT utility found that company 

commanders and platoon leaders who use CCTT are generally unaware that TSPs are even 

available (Mastaglio, Peterson, & Williams, 2004). Research might include reviews of the 

literature on automation acceptance, interviews among military leaders, and even experimental 

investigation of how trust and acceptance are formed. 

This set of recommendations will be well served by collaborative teams of training 

developers, psychologists, and military experts, similar to the groups that comprise the Training 

IPT. Work on the recommended courses of action will support a future training management 

that does not require all of the promised technologies to be delivered, and simultaneously 

advances our current understanding of and designs for future training management. These 

initiatives will help us to identify avenues of research that are promising, as well as those that 

will be pointless to explore. The interim solutions are not the stop-gap workarounds that are 

35 
independent of the envisioned goal. Rather, they are stepping stones on the path toward the 

eventual high-end solutions. 

36 
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39 
Appendix A 

 Acronyms and Abbreviations 

AAR After Action Review 

AI Artificial Intelligence 

ALMS Army Learning Management System 

ARI U.S. Army Research Institute for the Behavioral and Social Sciences 

ASAT Automated Systems Approach to Training 

ATIA Army Training Information Architecture 

ATIS Army Training Information System

ATRRS Army Training Requirements and Resources System

ATSC Army Training Support Center 

BCSS Battle Command Software Services

CALL Center for Army Lessons Learned 

CATS Combined Arms Training Strategy 

CCTT Close Combat Tactical Trainer 

CGF Computer-Generated Forces 

CITT Commander’s Integrated Training Tool 

CoLab Collaborative Laboratory 

CORDRA Content Object Repository Data Registration Architecture 

COTS Commercial-Off-the-Shelf 

CTIA Common Training Instrumentation Architecture 

DA Department of the Army

DARPA Defense Advanced Research Projects Agency 

DIMHRS Defense Integrated Military Human Resources System

DISA Defense Information Systems Agency 

DoD Department of Defense 

DTMS Digital Training Management System 

EMilpo Electronic Military Personnel Office 

ET Embedded Training 

FAQ Frequently Asked Questions 

FCS Future Combat Systems 

GIG Global Information Grid 

GIG-BE Global Information Grid Bandwidth Expansion 

HSOC Home-Station Operations Center 

HTM Human Training Manager 

ICW Individual Interactive Courseware 

IED Improvised Explosive Device 

A-1 
IEEE Institute of Electrical & Electronics Engineers 

IETM Interactive Electronic Technical Manual 

IMI Interactive Multimedia Instruction 

IPT Integrated Product Team

ITAPdB Integrated Total Army Personnel Database 

ITRS Individual Training Readiness System 

JADL Joint Advanced Distributed Learning 

KPP Key Performance Parameter 

LSI Lead Systems Integrator

METL Mission Essential Task List 

MS Microsoft 

MTO&E Modified Tables of Organization and Equipment 

NLP Natural Language Processing 

NN Neutral Networks 

NRFTT Networked Reconfigurable Full Task Trainers 

O/C Observer/Controller 

ORD Operational Requirements Document 

PDA Personal Digital Assistant 

PEOSTRI Program Executive Office for Simulation, Training, and Instrumentation 

PMO Performance Measurement Objects 

R&D Research and Development 

RFMSS Range Facility Management Support System

SCORM Shareable Content Object Reference Model 

SIDPERS Standard Installation/Division Personnel System

SOSCOE System of Systems Common Operating Environment 

TACP Tactical Command Post 

TADLP Total Army Distance Learning Program

TADSS Training Aids, Devices, Simulators, and Simulations 

TAMIS-R Training Ammunition Management Information System-Redesign 

TCC Training Common Components 

TDDT Training and Doctrine Development Tool 

TESS Tactical Engagement Simulation System

TMS(C) Training Management System (Concept) 

TO&E Tables of Organization and Equipment 

TRADOC U.S. Army Training and Doctrine Command 

TSP Training Support Package 

A-2 
UAMBL Unit of Action Maneuver Battle Laboratory 

UTMC Unit Training Management Component, also Unit Training Management 

Configuration 

WMI Warrior Machine Interface 

A-3