U.S. Army Research Institute 

for the Behavioral and Social Sciences 

Research Report 1823 

Knowledge Networks For Future Force Training: Illustration 

Of Searching, Retrieval, And Communication Concepts 

James A. Wall 

Randle D. Elms 

Keith E. Biggers 

Texas A&M University System 

Paul J. Sticha 

Human Resources Research Organization 

June 2004 

Approved for public release; distribution is unlimited.
U.S. Army Research Institute 

for the Behavioral and Social Sciences 

A Directorate of the U.S. Army Human Resources Command 

ZITA M. SIMUTIS 

 Director 

Research accomplished under contract 

for the Department of the Army 

Texas A&M University System 

Human Resources Research Organization 

Technical Review by 

Joseph Psotka, U.S. Army Research Institute 

 

NOTICES 

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NOTE: The findings in this Research Report are not to be construed as an official 

Department of the Army position, unless so designated by other authorized documents. 
REPORT DOCUMENTATION PAGE 

1. REPORT DATE (dd-mm-yy)

 June 2004 

2. REPORT TYPE

 Final 

3. DATES COVERED (from. . . to)

 16 August 2002 – 30 November 2003 

5a. CONTRACT OR GRANT NUMBER

DASW01-99-D-0012/Delivery Order 0019

4. TITLE AND SUBTITLE 

 Knowledge Networks for Future Force Training: Illustration of 

 Searching, Retrieval, and Communication Concepts 5b. PROGRAM ELEMENT NUMBER

 0603007A

5c. PROJECT NUMBER

 A792

5d. TASK NUMBER

 212

6. AUTHOR(S) 

James A. Wall, Randle D. Elms, Keith E. Biggers (Texas A&M 

University System), and Paul J. Sticha (Human Resources Research 

Organization) 

5e. WORK UNIT NUMBER

 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

 Human Resources Research Texas A&M University System

 Organization Texas Engineering Experiment Station 

66 Canal Center Plaza, Suite 400 332 Wisenbaker Engineering Research Center

Alexandria, VA 22314 300 TAMU 

College Station, TX 77843

8. PERFORMING ORGANIZATION REPORT NUMBER

 

10. MONITOR ACRONYM

 ARI 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 

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

ATTN: DAPE-ARI-IK

2511 Jefferson Davis Highway 

Arlington, VA 22202-3926 

11. MONITOR REPORT NUMBER 

Research Report 1823 

12. DISTRIBUTION/AVAILABILITY STATEMENT 

Approved for public release; distribution is unlimited.

13. SUPPLEMENTARY NOTES

 Contracting Officer’s Representative: Billy L. Burnside

14. ABSTRACT (Maximum 200 words): 

U.S. Army units are transforming to become a lighter, more mobile Future Force that can operate within joint and coalition

environments. To enable Future Force units to achieve successful deployment, the Army is developing knowledge networks to

provide access to a wide range of information across military operations. To use the vast amount of available information 

effectively, knowledge networks must include procedures that organize information, identify the most relevant information to

meet a user request, authenticate retrieved information, and allow the user to add new information to the network. This project 

develops and demonstrates a prototype knowledge network that illustrates methods to retrieve information needed to facilitate 

mission accomplishment, and to share this information among Soldiers and units. 

This research began with an information engineering activity that analyzed the functions and tasks of Future Force units to

identify information requirements. The effort proceeded to develop a prototype knowledge network incorporating relevant

existing technologies, including (a) Web crawlers, (b) searching and indexing, (c) rich site summary news feeds, (d) Web logs,

(e) user profiling, and (f) personal messaging. The prototype was demonstrated to four Army officers and enlisted personnel, 

whose impressions provided feedback used to revise the prototype and to make recommendations for future system

enhancements.

15. SUBJECT TERMS 

 Future Force Information engineering Training support Knowledge networks 

 Reach 

 SECURITY CLASSIFICATION OF 19. LIMITATION 20. NUMBER 21. RESPONSIBLE PERSON

16. REPORT

Unclassified

17. ABSTRACT 

Unclassified

18. THIS PAGE

Unclassified

ABSTRACT 

Unlimited

OF PAGES

62 

(Name and Telephone Number) 

Dr. Billy L. Burnside 

502-624-2613 

i 
ii 
Research Report 1823 

Knowledge Networks For Future Force Training: Illustration 

Of Searching, Retrieval, And Communication Concepts 

James A. Wall 

Randle D. Elms 

Keith E. Biggers 

Texas A&M University System 

Paul J. Sticha 

Human Resources Research Organization 

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

June 2004 

Army Project Number Personnel, Performance 

2O363007A792 and Training 

Approved for public release; distribution is unlimited

iii 
iv 
FOREWORD 

The U.S. Army has initiated transformation to a Future Force designed to be responsive, 

deployable, agile, versatile, lethal, survivable, and sustainable to meet the full spectrum of future 

missions. Ready access to repositories of information will be critical to the success of the Future 

Force. Knowledge networks will be needed to support such access, providing reach capabilities 

for entering knowledge into repositories as well as extracting it from them. For many years the 

U.S. Army Research Institute for the Behavioral and Social Sciences (ARI) has been involved in 

the identification of knowledge requirements and development of methods for satisfying them, 

particularly in the arena of training. Further identification and development needs to be 

accomplished for the Future Force environment. 

This report provides an overview of knowledge network capabilities needed by the Future 

Force and of an initial prototype network developed to provide such capabilities, with a focus on 

training support. It also provides the results of initial review and refinement of the prototype 

network, along with identification of further research and development needed. The work 

supporting this report was performed as part of Work Package 212, “Unit Training Technologies 

for Future Forces.” The relevant requirements document is a Memorandum for Record between 

the Deputy Director, Unit of Action Maneuver Battle Laboratory (UAMBL), U.S. Army Armor 

Center and Fort Knox and the Chief, ARI Armored Forces Research Unit at Fort Knox, entitled 

“Research and Development Related to Training Methods for Objective Force Units of Action 

Equipped with Future Combat Systems,” dated 10 September 2002. 

The results of this effort were demonstrated and briefed to representatives of the UAMBL 

on 28 and 29 May 2003; the project team refined the prototype based on feedback received 

during those sessions. The analysis completed and prototype developed should be highly useful 

to personnel in the UAMBL, the U.S. Army Training and Doctrine Command, and other 

agencies responsible for development and implementation of knowledge networks for the Future 

Force over the next several years. 

 STEPHEN L. GOLDBERG 

 Acting Technical Director

v 
vi 
KNOWLEDGE NETWORKS FOR FUTURE FORCE TRAINING: ILLUSTRATION OF 

SEARCHING, RETRIEVAL, AND COMMUNICATION CONCEPTS 

EXECUTIVE SUMMARY 

Research Requirement: 

The U.S. Army is changing the structure and organization of units to produce a lighter, 

more mobile Future Force that can operate within joint and coalition environments. The primary 

Future Force tactical element will be the Unit of Action (UA), roughly equivalent to a brigade of 

today. One key to the success of the Future Force is the ability of units and their commanders to 

have accurate and timely knowledge about the mission, terrain, status of friendly and enemy 

combatants, cultural characteristics, weather, and other situational information. Other 

information will be required to provide training and performance support to Future Force units. 

Much of this information will be available in digital format from any of a variety of private and 

military databases, file servers, and other electronically linked sites. 

To meet information needs for UA operations and training, the Army is developing 

knowledge networks (KNs) that will provide access to information from a wide range of sources 

to support the full spectrum of military operations. The KNs will incorporate various 

technologies to disseminate relevant information to meet the needs of Soldiers and their units. 

These include databases managed by Army organizations, links to relevant information sources, 

advanced search tools, innovative displays, and information-sharing capabilities. In addition, the 

KNs are planned to include procedures that allow the Soldier to add new relevant information 

and lessons learned to appropriate information repositories. 

The research described in this report seeks to evaluate the potential of current and 

emerging technologies for information storage, distribution, and retrieval to support Soldiers in 

the Future Force who must meet specific information requirements (IRs) or who must 

communicate to others the information obtained. The overall goal of the effort was to develop a 

prototype system that illustrates how these technologies could be used to support the information 

sharing process. 

Procedure: 

This research began with an information engineering activity that analyzed the functions 

and tasks of Future Force units to identify IR. In order to provide a reasonable demonstration, 

the analysis focused on two functional areas—Civil Military Operations (CMO) and Intelligence. 

The result of the information engineering activity was a scenario that would stimulate the 

requirement for information from a variety of sources. We then developed a prototype 

knowledge network that incorporated several existing and emerging technologies related to 

information search, retrieval, and dissemination in a web portal design. Methods employed in 

the prototype included: (a) Web crawlers, (b) several methods for information searching and 

indexing, (c) rich site summary news feeds, (d) Web logs, (e) user profiling, and (f) personal 

vii 
messaging. The information incorporated in the prototype knowledge network combined 

existing information from military and civilian sources with notional information designed to 

represent the future situation and units incorporated in the scenario. 

The prototype was demonstrated to four Army personnel: two commissioned officers 

and two non-commissioned officers. Their impressions provided feedback that was used to 

revise the prototype and to make recommendations for future enhancements of the system. 

Findings: 

To be useful, knowledge network development must be based upon clearly enunciated 

requirements expressed in terms of the minimum required capabilities. The information 

engineering activities used in the project ensured that the prototype would focus on the 

information required to meet mission needs. The prototype system illustrates how mission 

accomplishment can be supported by information search, retrieval, organization, and 

dissemination methods. 

Several activities should be conducted to foster further development and successful 

implementation of knowledge network concepts, including the following: (a) focus information 

engineering efforts on identifying authoritative sources of information, (b) integrate knowledge 

networks into Future Force command, control, communications, computers, intelligence, 

surveillance, and reconnaissance (C4ISR) networks, (c) complement rather than duplicate the 

requirements of other Future Force initiatives, (d) ensure that knowledge network tools are 

adaptable to a wide range of users through intuitive, user-friendly interfaces, and (e) conduct a 

variety of evaluation efforts at both the component and system levels. 

Utilization of Findings: 

The results of the research have identified several methods that can be incorporated in 

existing and future Army knowledge networks. The results of the demonstration provide 

preliminary evidence regarding the feasibility and acceptability of these methods. These results 

can benefit personnel in the U.S. Army Training and Doctrine Command (TRADOC), the Unit 

of Action Maneuver Battle Laboratory (UAMBL), and other agencies involved in the design and 

development of tools supporting the Future Force. 

viii 
KNOWLEDGE NETWORKS FOR FUTURE FORCE TRAINING: ILLUSTRATION OF 

SEARCHING, RETRIEVAL, AND COMMUNICATION CONCEPTS 

CONTENTS

Page 

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

Project Objectives.................................................................................................................... 2 

Organization of Report ............................................................................................................ 3 

Background.................................................................................................................................. 3 

Understanding the Future Force .............................................................................................. 3 

Developmental Concept....................................................................................................... 4 

Organization......................................................................................................................... 4 

Operations............................................................................................................................ 4 

Command, Control, Communications, Computers, Intelligence, Surveillance, and 

 Reconnaissance (C4ISR) ................................................................................................ 5 

Training................................................................................................................................ 6 

Information Dominance ....................................................................................................... 7 

Information Technology for Knowledge Networks................................................................. 7 

Data, Information, and Knowledge Systems ....................................................................... 8 

Overview of Technologies for Performing Network Searches............................................ 10 

Other Means of Information Distribution............................................................................ 13 

Formatting Knowledge for User Needs............................................................................... 14 

Summary of Development Activities........................................................................................... 16

Information Engineering.......................................................................................................... 16 

Scenario Driver........................................................................................................................ 18 

Capabilities Mapping............................................................................................................... 20 

Prototype Development ........................................................................................................... 24 

ix 
CONTENTS (continued)

Page 

Prototype Description .................................................................................................................. 26 

Knowledge Network System Overview .................................................................................. 26 

Main Page ............................................................................................................................ 27 

Searching.............................................................................................................................. 27 

Adding Information to the Network .................................................................................... 33 

Implementation Details............................................................................................................ 33 

Knowledge Network System Innovations ............................................................................... 38 

Knowledge Network Prototype Demonstration....................................................................... 38 

Method ................................................................................................................................. 39 

Results.................................................................................................................................. 40 

Interpretation of Results....................................................................................................... 41 

Discussion.................................................................................................................................... 42 

Integration of Knowledge Networks into Future Force C4ISR............................................... 43 

Need for Capabilities ........................................................................................................... 43 

Feasibility and Acceptability of Technology....................................................................... 43 

Utility of Technology........................................................................................................... 43 

Implementation Issues ......................................................................................................... 45 

Importance of Information Engineering in Knowledge Network Development ..................... 45 

Organization of Information Requirements......................................................................... 45 

User Interface....................................................................................................................... 45 

Research and Development Needs .......................................................................................... 46 

References.................................................................................................................................... 49 

Appendix A Acronyms...............................................................................................................A-1 

x 
CONTENTS (continued)

Page 

List of Tables 

Table 1. Data versus Information Retrieval ........................................................................... 9 

Table 2. Functions Baseline ................................................................................................... 21 

Table 3. Correspondence of Unit of Action (UA) Tactical Concepts to Universal 

Joint Task List (UJTL) Tactical Tasks..................................................................... 22 

Table 4. Capabilities Mapping—Partial Enumeration ........................................................... 24 

List of Figures 

Figure 1. Cognitive hierarchy. ................................................................................................. 8 

Figure 2. Information retrieval model...................................................................................... 10 

Figure 3. Standard search engine structure.............................................................................. 11 

Figure 4. Information engineering schema for prototype Knowledge Network...................... 18 

Figure 5. Information engineering—scenario driver............................................................... 19 

Figure 6. Basic analytic framework......................................................................................... 22 

Figure 7. Capabilities mapping................................................................................................ 23 

Figure 8. Knowledge network banding effect ......................................................................... 26 

Figure 9. Knowledge network - main page.............................................................................. 28 

Figure 10. General search .......................................................................................................... 29 

Figure 11. Search results............................................................................................................ 30 

Figure 12. Concept search ......................................................................................................... 31 

Figure 13. Menu search ............................................................................................................. 32 

Figure 14. Generic Web search.................................................................................................. 33 

Figure 15. Knowledge network framework............................................................................... 34 

Figure 16. Potential reduction of time requirement for decision cycle...................................... 44 

xi 
KNOWLEDGE NETWORKS FOR FUTURE FORCE TRAINING: ILLUSTRATION OF 

SEARCHING, RETRIEVAL, AND COMMUNICATION CONCEPTS 

Introduction 

The U.S. Army is changing the structure and organization of units to produce a lighter, 

more mobile Future Force that can operate readily within joint and coalition environments. The 

goal of the transformation is to make the force more responsive, deployable, agile, versatile, 

lethal, survivable, and sustainable. These characteristics will enable Future Force units to see 

and understand the environment quickly, act effectively, and finish decisively. The primary 

Future Force tactical element will be the Unit of Action (UA), roughly equivalent to a brigade of 

today (U.S. Army Training and Doctrine Command [TRADOC], 2003b). 

One key to the success of the Future Force is the ability of units and their commanders to 

have accurate and timely knowledge about the mission, terrain, status of friendly and enemy 

combatants, cultural characteristics, weather, and other situational information. The primary 

asset that will allow Future Force units to achieve information dominance is the command, 

control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) 

network. The C4ISR network will enable Future Force commanders and command groups to 

receive, process, and transmit information rapidly and to make and adjust decisions on the move. 

They will use the C4ISR network to plan operations, to control placement and movement of

manned and robotic sensing and firing elements, to effect the engagement of targets at extended 

ranges, and to communicate information and orders. 

Other information will be required to provide training and performance support to Future 

Force units. Much of this information will be available in digital format from any of a variety of

private and military databases, file servers, and other electronically linked sites. Using this 

information, UA elements will be able to practice operations and conduct training even while 

deploying or deployed, thereby extending staff capabilities. Relevant information may include 

doctrinal materials, recommendations by subject matter experts (SMEs), intelligence on a 

particular area or enemy, training materials such as training support packages (TSPs), and tools 

for tailoring available training materials and operational tactics, techniques, and procedures 

(TTPs) to meet unit needs (U.S. Army Armor Center [USAARMC], 2003). 

Because of the amount of information expected to be available and the number of 

information sources, obtaining the answer to a specific information requirement (IR) could be a 

complex process. A search may produce conflicting information from different sources, as well 

as information with varying degrees of accuracy and recency. The process of formulating a 

search, reviewing the information obtained, and verifying the relevancy and accuracy of the 

information could take hours or possibly even days, without any guarantee that the information 

produced would be completely relevant to the question that prompted the search. Searchers 

would only know what information was retrieved, not necessarily what other information 

supporting their IR was available from other sources to which they did not have access. 

To meet information needs for UA operations and training, the Army is developing 

knowledge networks (KNs) that will provide access to information from a wide range of sources 

1 
to support the full spectrum of military operations. The KNs incorporate databases that are 

managed by Army organizations, links to other relevant information sources, advanced search 

tools, innovative displays, information-sharing capabilities, and other technologies to get 

relevant information to meet the needs of Soldiers and their units. When deployed with and 

accessible from Future Combat Systems (FCS), KNs could be key enablers for the Future Force 

that will allow Soldiers to search for, obtain, and share their knowledge to enhance mission 

accomplishment. 

Critical to the success of knowledge networks within the Army are procedures that will 

allow a Soldier to access efficiently the information required to perform his or her mission. The 

procedures must be able to identify authoritative information that is appropriate for the user’s

function in the unit, for the situation at hand, and for the user’s preferences for content and 

format of that information. In addition, the knowledge network should include procedures that 

allow the Soldier to add new relevant information and lessons learned to appropriate repositories. 

Information added to the network could include intelligence gained, lessons learned, and 

modifications of TTPs or TSPs. Such capabilities have been described as reaching back for and 

pushing forward information. The general term “reach” is used to describe the capability for 

multi-directional exchange of knowledge network information (Department of the Army [DA], 

2002a). 

Project Objectives 

The utility of the vast amount of digital information that will become available to 

Soldiers in the Future Force depends on the development and implementation of procedures to 

efficiently access, manipulate, and disseminate it. The research described in this report seeks to 

evaluate the potential of current and emerging technologies for information storage, distribution, 

and retrieval to support Soldiers in the Future Force who must meet specific IRs or who must 

communicate to others the information obtained. The overall goal of the effort was to develop a 

prototype system that illustrates how these technologies could be used to support the information 

sharing process. 

To support the overall goal, it was necessary to understand the information that would be 

required by the Future Force, to evaluate candidate technologies for managing knowledge 

networks, to implement promising technologies in a prototype, to evaluate and enhance the 

prototype, and to document the results and their implications. The following technical objectives

specify the project requirements. 

• To complete a functional description and design of knowledge networks for Future 

Force units, with a focus on UA training. 

• To develop a prototype knowledge network that illustrates capabilities that can be 

used to integrate available information to support selected UA training requirements. 

• To try out and refine the prototype knowledge network, demonstrating its capabilities 

for rapid reconfiguration and expansion. 

• To document findings along with recommendations for future research and 

development. 

2 
The resulting prototype illustrates the use of technologies incorporating freely available

software and a combination of actual and hypothetical data. The hypothetical data represent 

some of the types of information expected to be available to the Soldier in the Future Force. To 

provide this illustration within the fiscal constraints of this effort, we could not obtain the most 

advanced implementations of each relevant technology. Nevertheless, the prototype capabilities 

provide a concrete example of how these technologies could be used. Concepts included in the 

prototype could be incorporated into current or planned systems, or could form the basis of a 

new system. It was not intended that this research would specify how the prototype should be 

implemented. 

Organization of Report 

This report continues with an overview of the changes that are encompassed in the 

transformation to the Future Force, focusing on the need for information that will govern the 

requirements for future knowledge networks. It then presents a summary of technologies that 

may help to meet the information needs of the Future Force. Most of these technologies already 

exist in some form, and allow Internet users to query, search, sort, and disseminate information 

on the World Wide Web. 

The report then describes the procedures that were used to conduct the information 

engineering and prototype development activities. An analysis of IRs related to the Future Force 

forms the basis for the design and implementation of the prototype network. We describe the 

prototype and report the results of a demonstration to Army commissioned and noncommissioned officers (NCOs). Finally, we summarize the results and discuss their implications 

for future research and the eventual implementation of knowledge networks for the Future Force. 

Background 

This section begins by describing characteristics of the Future Force that might relate to

its information needs. It then describes existing technologies for information retrieval and 

dissemination that could be the basis of a KN for the Future Force. 

Understanding the Future Force 

Because the development of concepts of operation for the Future Force and the FCS is 

ongoing, certain details of UA organization and functions have not yet been specified in planning 

documents. For example, a review of documentation found limited details regarding staff 

organization and functions at brigade and battalion levels. Although the Operational and 

Organizational (O&O) Plan (TRADOC, 2003b) provides details on how the UA staff is 

organized during combat operations, the staff cell descriptions are at a collective rather than 

individual level. We anticipate that the inevitable changes in Future Force concepts that will 

occur as they evolve will have little or no technical impact on this research. However, some of 

the details of the organizations and operations of the UA mentioned in this report may have been 

modified by documents that were prepared after the review was completed (approximately June 

2003). 

3 
Analysis of the O&O Plan, the Operational Requirement Document ([ORD] USAARMC, 

2002b) and the System Training Plan ([STRAP] USAARMC, 2003) highlighted the following 

areas as being central to understanding the Future Force organization and operational concept: 

• Developmental Concept, 

• Organization, 

• Operations, 

• C4ISR, 

• Training, and 

• Information Dominance. 

Developmental Concept 

The Army currently performs a full spectrum of operations and missions—including 

peacekeeping, homeland security, security assistance, and ground combat—using combinations 

of nine ground combat formations. The variety of unit compositions complicates transport, 

resupply, survivability, and terrain restrictions. The UA will be a flexible unit capable of 

performing most missions, with the exception of those employing Special Forces (SF), Rangers, 

or airborne forces (TRADOC, 2003b). The UA is designed to operate alone or as a part of a 

joint team against any level of threat. The Future Force team must be strategically and 

operationally responsive, rapidly deployable, and able to change patterns of operations quickly. 

The forces must have the ability to generate superior combat power by leveraging the synergy of 

maneuver, firepower, protection, and leadership. They must also be dominant over the enemy, 

informed by reliable situational awareness and understanding (DA, 2002a). 

Organization 

The UA is not designed to be a fixed organization. It has the capability to command and 

control up to six maneuver battalions. The UA will also be able to draw on a range of supporting 

capabilities from a Unit of Employment (UE) or from the Joint Task Force (JTF) to perform a 

variety of missions including Psychological Operations and Civil Affairs. As a flexible 

structure, the UA will be tailorable for specific missions by adding to or changing its capabilities. 

Its C4ISR architecture will enable the UA to increase its span of control. Maneuver-sustainment 

support units such as the Forward Support Battalion will also be tailorable with additional 

capabilities when required to support UA augmentation (TRADOC, 2003b). 

Operations 

The UA will normally fight under the command and control of a divisional UE (in some

situations a Corps/JTF). The UE will employ UAs to achieve tactical objectives. The UA will 

integrate organic and supporting Intelligence, Surveillance, and Reconnaissance (ISR) fires, and 

maneuver to close with and destroy the enemy. The FCS ORD (USAARMC, 2002b) lists the 

required operational capabilities. Several of these requirements are summarized in the following 

discussion. 

4 
 Deployability. The UA is designed and will be equipped to support rapid deployment to 

austere theaters with unimproved entry points, without relying on fixed ports or staging bases. 

The unit will be able to deploy anywhere in the world within 96 hours of alert. A force as large 

as five divisions will be available in theater within 30 days of alert. Because of the reduced size

and weight of vehicles, compared to existing inventory, the entire unit can be transported by air 

anywhere in the world. A unit equipped with FCS will be able to use its time en route to 

rehearse the mission plan (Riggs, 2003). Upon arrival in the area of operations, the UA is 

designed to be capable of self-sustained operations for three to seven days. Furthermore, it is 

designed with the durability, endurance, and stamina to fight battles and engagements for the 

duration of a campaign. 

 Agility and Versatility. Unit of Action capabilities will permit future commanders to 

develop plans for meeting a given situation before making contact, to maneuver to positions of

advantage and, when ready, to initiate decisive action (TRADOC, 2003b). Forms of maneuver, 

tactical formations, and movement techniques common to today’s force will endure, but the TTP 

for implementing them will change. The UA will be able to conduct the full spectrum of military 

operations, including deterrence, homeland security, stability operations, support operations, 

small-scale contingency (SSC) operations to restore peace and stability, and global war on 

terrorism. The UA will be optimized for offense in major combat operations (MCO). It is 

organizationally designed to conduct these operations in all types of terrain and in any weather 

condition. 

 Maneuverability. Key factors in maneuverability include decisiveness and the ability to 

move in both day and night conditions, on all terrain and in all weather conditions, synchronized 

with Army and Joint fires and ISR. Other capabilities to consider include mobility and the 

conduct of integrated combined operations to include diverse areas such as urban terrain. 

Another factor in optimum maneuverability is effective C4ISR support throughout. 

Command, Control, Communications, Computers, Intelligence, Surveillance, and 

Reconnaissance (C4ISR) 

To accomplish its mission, and to handle the transitions in this type of warfare and retain 

the necessary momentum and initiative, the UA will need superior situational understanding 

(TRADOC, 2003b). A key to Future Force units achieving information dominance will be the 

C4ISR network. This network will enable Future Force commanders, command groups, and 

their staffs to receive, process, and transmit information rapidly to make and adjust decisions on 

the move. 

The communications provided to the UA will enable it to link into the Global Information 

Grid (GIG) at any time to include a reach capability for a wide range of data and information 

(TRADOC, 2003a). The GIG is a globally interconnected set of information capabilities and 

processes and includes all owned and leased communications and computing systems and 

services, software, data, security services, and other associated services necessary to achieve 

information superiority (IS). 

5 
Battle Command on the Move (BCOTM) is a key capability contributing to agility and 

versatility. The Battle Command system represents the migration of all fielded and 

developmental Army command and control systems into one fully integrated and interoperable 

system with seamless connectivity from the National Command Authority (NCA) to the foxhole. 

It will allow commanders in the UA to access information and decision-making tools and to 

communicate decisions much faster than today. Widely distributed operations on future 

battlefields will require significantly increased deployability of command and control functions. 

The BCOTM will be accessible from anywhere on the battlefield, making it possible for a much 

smaller and more mobile unit to perform the functions formerly originating at command posts 

(DA, 2002a; TRADOC, 2003a). 

Training 

Training of Future Force Soldiers and units will be supported by the use of embedded 

training and electronic centrally distributed training products. The vision for Future Force 

training is to provide embedded training down to system level (USAARMC, 2003). Command 

post exercises and command field exercises (CPX/CFX) will be driven by prepared scenarios 

available for use before, during, and after deployments. Additionally, the systems will be used to 

embed operations order (OPORD) data that facilitate rehearsals. Another factor that must be 

considered is that the training materials are developed and made available to UA units by Army

training developers (DA, 2003). 

 Networkable, Reconfigurable, Full Task Trainers (NRFTT). The NRFTT for the FCS 

family of systems will be devices used mainly at institutions where, depending on class size and 

student throughput requirements, a training device is more cost effective than platforms and thus 

can be more available than platforms with embedded training systems. However, the NRFTT 

and embedded training share the several qualities. The NRFTT, like platform embedded 

training, will provide a virtual training capability to support individual, crew, and small unit 

competencies when parts of the combined arms team are not available. The NRFTT will be fully 

operational without the need for external components to transport or install on the FCS 

platforms. The NRFTT will be capable of supporting training at individual through brigade 

levels, and will be capable of simulating and stimulating tactical systems at all echelons. To 

ensure that training in the NRFTT is the same as embedded training on the platform, the NRFTT 

will operate with the same software used in the operational/embedded training system and 

present the same man-machine interface to the Soldier (TRADOC, 2003b; USAARMC, 2003). 

Training Distribution System. The FCS ORD envisions an information system down to

the battalion nodes and all FCS manned systems, including the individual Soldier (USAARMC, 

2002b). The system will provide integration across the spectrum, in a secure mode for planning 

and rehearsal of missions, from alert to employment. The requirements for the UA training 

system include the embedded Tactical Engagement Simulation System (TESS), which covers 

live, constructive, and virtual training methods. The ORD states that the FCS will have an “onboard” repository of technical manuals (TMs), TTP documents, field manuals (FMs), training 

plans (TPs), and TSPs, interactive multimedia instruction and courseware, and collective training 

information. The ORD details plans for 24/7 support from the centralized sources for scenarios 

6 
tailorable to the unit Mission Essential Task List (METL), to include Computer Generated 

Forces (CGF). The plan calls for fielding a complete set of TSPs. 

Information Dominance 

Information operations are so critical to the Future Force that the UA staff will have a 

dedicated IS cell. Key features that contribute to gaining information dominance include: 

• Tactical Database. 

• Common Operational Picture (COP). 

• Home Station Operations Center (HSOC). 

• Command Information System (CIS). 

Tactical Data and Common Operational Picture (COP). The Distributed Information 

Database (DIDb) will provide users a shared awareness of key battlefield information. Access to

the DIDb will be available to all echelons of the UA. Through a constant push and pull of

information, the DIDb will fuse information from multiple sources to produce the COP. It will 

also enable access and distribution of information throughout the force in standardized formats. 

The databases will support a two-way collaborative process in which lessons learned are 

incorporated into the database (TRADOC, 2003b). 

Home Station Operations Center (HSOC). Installation HSOC will enable distributed

information sharing among the sustaining base and deployed forces during all phases of an 

operation. Before deployment, these fixed facilities can collect and process large volumes of 

data, such as terrain mapping and common databases that must be pre-loaded down to platform

level prior to deployment. The HSOC will be the focal point for the synchronization of all 

training resources required for the execution of training tasks at all affiliated locations.

Communication Information System (CIS). The CIS in the UA will consist of the 

infrastructure, organization, personnel, and components that collect, process, transmit, and 

disseminate information. It will enable the commander to view and understand his battle space, 

communicate his intent, lead his forces, and disseminate pertinent information throughout the 

UA. This collaboration feature will be key to shortening the normal decision cycle. The CIS 

will permit continuous fusing, monitoring, and dissemination of information from a variety of 

sources, through an uninterrupted, robust, multi-layered communications system (U.S. Army

Future Combat Systems Program Office, 2002). 

Information Technology for Knowledge Networks 

As the previous discussion indicates, the UA will operate in an environment 

characterized by an abundance of information from multiple sources. Some of these sources will 

be interoperable with UA systems by design, but others will use a variety of processes and data 

formats. Soldiers in the Future Force will need to search data from a variety of sources and 

quickly identify and retrieve the most relevant, timely, and accurate information that relates to a 

particular IR. The information must be provided to them in a format that they can easily 

7 
understand and incorporate in their plans. They may also need to share this information with 

others and to incorporate new information that others can access at a later date. 

A knowledge network combines repositories of information with methods to organize, 

search, retrieve, share, and update the information in the repositories. It provides a common 

interface to knowledge that may be contained in multiple systems and stored in a variety of 

formats. The KN must include sophisticated methods for retrieving useful information in a 

usable format. It must support collaboration among people using different and perhaps 

incompatible information platforms. Finally, it must support dissemination of information as 

well as retrieval. If successfully implemented, KNs can provide user-friendly access to vast 

repositories of existing information (and data) to UA elements involved in Future Force training. 

They will make it possible to query and search these sources in a timely and efficient manner and 

to rank and present the most relevant results to the user. 

Data, Information, and Knowledge Systems 

Implicit in the term, knowledge network is the notion that what is provided by a 

knowledge system to its user is somehow enhanced or qualitatively different from what is 

provided by a database or information system. The terms data, information, and knowledge are 

often used interchangeably, or one is defined in terms of the other. Figure 1 shows what is 

commonly referred to as the Cognitive Hierarchy (DA, 1999) in order to distinguish among these 

terms. This pyramid is typically used by organizations in the corporate world, the military, and 

others to describe the process in which knowledge and understanding are preceded by the 

existence of data and information. 

Information that has been

organized to support

decisions or action

Data that are processed and placed

into a specific situational context

Data

Understanding

Knowledge

Judgment

Cognition

Processing

Information

Global Information Environm Global Information Environment

Use of judgment to give knowledge relevance

within a specific context.

Objects such as numbers, 

characters, or images that record

aspects of the environment

Information that has been

organized to support

decisions or action

Data that are processed and placed

into a specific situational context

Data

Understanding

Knowledge

Judgment

Cognition

Processing

Information

Global Information Environm Global Information Environment

Use of judgment to give knowledge relevance

within a specific context.

Objects such as numbers, 

characters, or images that record

aspects of the environment

Figure 1. Cognitive hierarchy. 

The term, data, represents the most concrete representation of aspects of the environment 

in a format that can be processed by a human or a machine, and that can be transmitted on some

8 
communication channel. Individual data elements have no meaning in themselves; it is only by 

processing that data can be interpreted and the meaning understood. In transforming data to 

information, the elements are placed into situational contexts or categories that allow them to be 

interpreted. For example, a number such as 324.2 has no meaning in itself. However, if it is 

known that the number represents a weight, a distance, or a bank balance, then the context 

provides meaning to the number, which may then be viewed as information. Knowledge 

requires further processing to combine information from different sources and place it in a 

format that supports decision-making and action. In addition, knowledge is generative, in that a 

person’s knowledge includes rules that can be used to create new knowledge from existing 

knowledge or information. Finally, understanding requires additional processing of knowledge 

to give it relevance within a given context. 

These terms may often be confused, and the point could be made that many so-called 

information retrieval systems should be more appropriately labeled as data retrieval systems (van 

Rijsbergen, 1979). Table 1 shows the comparison of attributes of a data retrieval system

compared to those of an information retrieval system. As the table indicates, information 

retrieval is a much more difficult task than data retrieval. Data retrieval typically requires simple 

retrieval rules with easily specified matching criteria. Concepts for information retrieval are 

often more difficult to define. They may be probabilistic, so that it is not known for certain 

whether a particular piece of information matches the conditions of a query. Distinctions 

between similar concepts may not be absolute, and different information elements may partially 

match several apparently distinct concepts. 

Table 1 

Data versus Information Retrieval (van Rijsbergen, 1979) 

Function Data Retrieval 

Characteristic 

Information Retrieval 

Characteristic

Matching Exact Match Partial Match; Best 

Match 

Inference Deduction Induction

Model Deterministic Probabilistic

Classification Monothetic Polythetic

Query Language Artificial Natural

Query Specification Complete Incomplete

Items Wanted Matching Relevant

Error Response Sensitive Insensitive

Knowledge networks expedite the acquisition of data and information and create 

conditions that speed the development of knowledge and understanding, and hence produce more 

efficient and effective decision-making. While there may be situations in which it could be said 

that a KN retrieves knowledge, in the vast majority of cases it is more accurate to say that a KN 

9 
retrieves data and information in a way that facilitates the construction and utilization of 

knowledge. In order to facilitate knowledge generation, the KN must identify the most relevant 

information, organize it in a way that supports decision-making and action, and present it to the 

user in a fashion that communicates both the relevance of information elements and their 

interrelationships. 

Overview of Technologies for Performing Network Searches 

A typical information retrieval model is presented in Figure 2. This model will serve as a 

template for those areas that were investigated in this project to determine the state of the art of 

information retrieval technologies. It is necessary to determine whether the maturity of each will 

be sufficient to meet Future Force training requirements. The model shows two views into the 

overall system: 

1. The view of the database manager, who is responsible for locating relevant data 

sources and transforming the text into an index that accommodates quick searches of 

large volumes of data. 

2. The view of the user, who must be provided an interface into the system and given 

the means to construct a search query. The retrieved data should then be ranked and 

presented to the user in the most effective manner. 

Stored

Index

Search

Rank

Display

User Pre-process

Data

Index

Data

Query 

Interface

Boolean-matched

Results

Ordered

Results

Query Parameters

Query Results

Stored

Index

Search

Rank

Display

User Pre-process

Data

Index

Data

Query 

Interface

Boolean-matched

Results

Ordered

Results

Query Parameters

Query Results

Figure 2. Information retrieval model. 

10 
In general, Web-based search engines are derived from a standard architecture (Figure 3). 

Each has its own unique features, but their overall organizations are the same. These engines are 

composed of the following primary components: 

• Crawlers that traverse the Web looking for new pages and updates to existing cached 

pages. Cached pages refer to those Web pages retrieved previously from the network 

and stored locally for more convenient (and quicker) access. 

• A method of understanding different file format types (Hypertext Markup Language 

[HTML], Postscript [PS], Portable Document Format [PDF], etc.) and converting 

them into a single understandable format (i.e. flat text). 

• An index to store a lexicon of keywords used by crawlers to identify relevant 

documents, their location within a particular document, and other specific details 

about word occurrences. 

• A means for users to query the index for matching results. 

• A means to rank/sort the results returned from a query. 

Internet

Rank/Sort

Results Crawlers

Index

User’s

Query

Results

Pre-process

Search Engine

Internet

Rank/Sort

Results Crawlers

Index

User’s

Query

Results

Pre-process

Search Engine

Figure 3. Standard search engine structure.

11 
Typical search engines have crawlers continually traversing the Web looking for new 

pages not yet indexed and updates to pages that are cached and have already been incorporated 

into the index. Once found, these documents must be processed and stored for future use. This 

processing involves several steps. First the documents must be converted to a common format 

that is understandable by the system (usually flat text). Documents of different formats 

(Microsoft Word and PowerPoint, PDF, HTML, etc.) must all be converted into this common 

format so they can be processed and analyzed. Then the document must be parsed and the 

keywords extracted. There are different algorithms for doing this, but in general all of the “stop” 

words (common words such as “the,” “and,” “for,” etc.) must be removed from the document 

and then the remaining words are stemmed (reducing a word to its root form). There are many 

different stemming algorithms, but the most popular by far is the Porter Stemmer (Porter, 1980). 

The Porter Stemmer uses a set of rules to automatically remove different suffixes within words 

to reduce them to their root forms. For example, the words connected, connecting, connection, 

and connections would all be reduced to the base connect. Through the removal of “stop” words 

and the execution of the stemming process, the set of terms that must be processed within a 

document is greatly reduced. In addition, this process results in an overall smaller index and 

therefore speeds up queries by reducing the number of terms that must be evaluated when 

executing a search. In addition, the smaller index takes up less memory and becomes more 

manageable. 

The documents must be cached so the index can periodically be updated as new pages are 

found. For example, GoogleTM rebuilds its index monthly, on average, and each update usually 

takes several days to complete. If an index is large, such as Google’s, updating can be complex, 

balancing the need to keep the old index accessible while the new index is being generated. 

Once the indexing update is complete, the new documents are incorporated into the new index 

and the engine points users to the new index. The Internet is a dynamic structure, with search 

engines continuously locating new information, rebuilding and updating the index, and making it 

accessible to users. 

Useful search engines and the indexes behind them must be accessible to users. Typical 

Web-based engines perform full-text keyword-based searches in which a user enters a set of 

keywords into the search box, and the engine returns all pages matching the query. The engine 

must take these keywords and evaluate the index, returning all those sources that match the 

query. If a simple, conjunctive Boolean-based search were used, then every document 

containing all of the words entered would get returned. For efficiency, the engine checks the 

index and looks at the number of documents that are associated with each word entered into the 

query. It then selects the smallest document list to start the process. Since queries are generally 

conjunctive, all words that appear in the query must appear within the document, so the list 

returned to the user will at most be the size of the smallest list. This list is then intersected with 

the document lists for all the other words contained in the query. Finally, a set of documents that 

incorporate all the words entered is returned. As one would imagine, this could be quite a timeconsuming process as the document lists become larger. Different algorithms have been 

developed to help speed up this processing by compressing the manner in which indexes are 

stored, using hashing techniques to speed up the process of searching, and so forth (Witten, 

Moffatt, & Bell, 1999). 

12 
To make search engines more usable, they typically have some means of ordering the 

results before they are presented to the user. This allows the engines to rank items by their 

estimated relevance, reducing the need for the user to look at many irrelevant documents before 

the sought after information is found. Ranking algorithms come in many different forms. They 

can look at different characteristics of the page, such as frequency counts of words and how and 

where the keywords get used within the page (how many times a keyword gets used, is it in a 

heading, is it bold or italicized, etc.; Brin & Page, 1998). GoogleTM incorporates many of these 

ideas to achieve its page rankings. 

One of the most popular page-ranking algorithms, called PageRank, calculates an 

“importance value” for a page based on the number of links from other pages pointing to the 

page. Highly linked pages are considered to be more important than pages linked from only a 

few others. The system also weights the page’s importance based on the ranking of the pages 

pointing to it. This calculation may present a problem because we will not know what ranking 

those pages have until the pages pointing to them have their rank calculated and so on. 

PageRank solves this problem by using a simple iterative algorithm (Page, Brin, Motwani, & 

Winograd, 1998). 

Google’s PageRank provides fairly high quality search results; however, important pages 

mean nothing to a user if they don't match his or her query. Therefore, GoogleTM combines 

PageRank with sophisticated text-matching techniques to find pages that are both important and 

relevant to a query. GoogleTM goes far beyond counting the number of occurrences of a term per 

page and examines all aspects of the page's content (and the content of the pages linking to it) to 

determine if a page is a good match (Brin & Page, 1998). It evaluates where the search terms 

appear within the document and categorizes the term’s appearances into one of several categories 

(i.e., title, anchor, uniform resource locator [URL], plain text, etc.). Finally, it calculates a score 

for the page based on where the words appear. 

Although current Web search tools provide a more efficient way to gather information as 

compared to manual alternatives, easier and more intuitive ways to gather information need to be 

investigated. By applying various emerging technologies, such as Bayesian and vector indexing, 

to the standard search process and allowing a user to obtain and digest information more easily, 

knowledge and understanding can be acquired more efficiently. Gained knowledge and 

understanding then become the basis for sound decision-making. 

Other Means of Information Distribution 

Users want easier, more automated access to information without the need to have to go 

out and pull it down. With many of the new technologies available on the Web, users can log on 

to a system and instantly be alerted that new information that affects them or their work is 

available for downloading. Two popular forms of communicating information across the Web 

that are currently being used are blogs (short for Web Logs) and rich site summary (RSS) news 

feeds. 

Web logs. Blogs are emerging tools that take advantage of some of the major features

that the Internet provides (Siemens, 2002). Blogs support knowledge sharing and 

13 
communication by allowing information to be published. In general, blogs contain dated entries 

in which a user can post some piece of information. Once the information has been posted, 

others can read and comment back to the originator. These comments are visible by all and 

many times online discussions can be started. These blogs can be used in many different ways to 

support knowledge sharing and knowledge network management, learning, and experience 

tracking. For example, deployed troops could post information about current conditions in the 

field and make equipment recommendations that could be available to units that are in the predeployment phase. This could provide valuable information sharing that might never happen 

otherwise. 

Rich Site Summary news feeds. Another form of information sharing is the RSS format. 

The RSS is an extensible markup language (XML)-based format that describes the metadata 

about a Web site and has become a popular method of distributing news headlines on the Web 

(Lewin, 2000). Currently, RSS feeds are most prominently used by news organizations and Web 

sites for distributing headlines, but are also being used by smaller organizations to alert 

subscribers to the availability of new information or updates to existing information. The RSS 

feeds could easily be used to distribute both horizontally and vertically within military

organizations. A user would simply have to subscribe to an information channel that distributes 

a feed of interest, and as updates become available, the user would be alerted automatically. 

These feeds allow for easy information push without the need for the user to check for updates 

on multiple sites. 

Formatting Knowledge for User Needs 

Portal-style Web pages, sites that collect a selection of services and resources into a 

single location (such as Yahoo.com’s main page); have become quite popular by providing a 

user with a structured and customizable display of information. User profiling and agent 

technology provide other methods to provide information that specifically meets a user’s needs 

in a format that the user prefers. 

Portal interfaces. A popular means of structuring information on the Web is through the 

use of portal-style interfaces. Portals are standard, frequently used Web sites that offer a user a 

variety of services and resources collected into a single location (Levine, 2002). One of the most 

popular examples of a portal is the Yahoo!® main page (www.yahoo.com). This page provides 

the user with many different types of information, links to various types of services, and is 

simple to use and understand. Portals are not limited to public sites and can be used within 

business Intranets to allow easy access to tools and information for users. Using a common 

framework for information sharing and access, the portal allows the organization to integrate 

different technologies into a common framework. The ultimate goal of portals is to offer the 

user easy access to a range of information and resources with a single click of the mouse. Portal 

sites should be very well organized and possibly even customizable by the user. For example, if 

news headlines from different sources were displayed, then the user should have the ability to 

select what sources get used and what types of articles get returned. Ideally, each user could set 

up his own set of links to preferred pages through customization of the portal page. 

14 
User profiling. The concept of tailoring information to a user’s interests or needs is a 

developing technology slowly moving into the mainstream in extending access to information 

storage and retrieval. Numerous current academic projects are researching different profiling 

methods. Some store on a user’s machine informational “cookies” that capture details about 

sites the user visits. From the collected data, the researchers attempt to predict users’ interests 

for ads and headlines. Others use machine-learning and data-mining techniques to help rank the 

pages returned by a search. The use of intelligent agents (semi-autonomous computer programs 

capable of carrying out one or more tasks specified by the user) for dynamic information 

searches is growing in popularity. There are many different focuses within these research 

projects, but the common goal is to make searching for and obtaining information an easier 

process. 

Profiling a user and tailoring information based on known interests and behaviors is an 

active area of research. Currently, some sites such as Amazon.com and Yahoo.com attempt to 

profile users by storing information about the sites they visit or products they review and then 

display ads or products that are representative of those interests in the hopes that a user will be

attracted to a particular product. However, these profilers are somewhat simplistic. 

More sophisticated user profilers being developed as part of current research projects far 

surpass the approaches used by Amazon.com and Yahoo.com. These projects are developing 

more advanced techniques for categorizing information content and personalizing information 

ratings based on user interest categories. One such system is Alipes, a personalized newsagent 

that gathers articles periodically from various online news sources and filters them on behalf of a 

user’s interests (Widyantoro, Yin, et al., 1999). In Alipes, a user profile is represented by three 

descriptors: a positive descriptor, a negative descriptor, and a long-term descriptor. The positive 

and negative descriptors represent a user’s short-term interests (which could drastically change 

over a short period of time) and the long-term descriptor represents a user’s relatively stable 

interests (Widyantoro, Ioerger, & Yen, 1999). Hence, Alipes is able to adapt to the dynamic 

nature of a user's interests, which may change slowly or very suddenly over a varying period of 

time and continually recommend interesting articles to the user (Widyantoro, Ioerger, & Yen, 

2001). Alipes has been demonstrated to be quite effective at ranking news sources with respect 

to a user’s interests. 

Agent technology. Another technology of potential use in constructing knowledge 

networks is the use of agents for locating and filtering information before it is presented to the 

user. One example is Constructing Intelligent Agents (CIAgent), a system developed as part of 

IBM’s Agent Building and Learning Environment (ABLE). It is an agent-based framework for 

creating and using user profilers (Bigus, 2001). The CIAgent is able to function as a personal 

assistant, an information filter, and a market buyer or seller. The CIAgent filters news based on 

a set of user-provided keywords. A user's profile evolves over time by using alternative 

modeling algorithms referred to as self-organized maps and neural networks. 

Another area of information retrieval research is focused on how agents can be used in 

the process of information fusion. Agents can collect diverse information from multiple sources 

and merge it into a single representation to be presented to the user. For example, a user could 

tell his agent that he wants to go on a date and see a particular movie around 8 P.M. on Friday 

15 
night. The agent could then find the times from various theaters near the individual’s home, 

locate restaurants near the theater that the agent determines the user would like (based on known 

user preferences), generate maps to the various locations, and fuse them into a common map. 

The use of these types of agents would eliminate the need for a user to go out and individually 

locate each of these pieces of information. 

As the technologies used for information storage and retrieval continue to improve, the 

Future Force could apply many of them to developing knowledge networks. The prototype 

developed under the auspices of this research incorporates a representative sample of such 

technologies. 

Summary of Development Activities 

This research began with a comprehensive analysis to identify and organize IRs for 

specified UA functions. With the information gathered, we constructed a matrix that associated 

IRs with tasks, enabling us to construct a scenario that would stimulate Soldier queries for a 

variety of information. Based on this scenario, we designed, developed, and demonstrated a 

prototype knowledge network. 

Information Engineering 

We used information-engineering processes to identify the IRs for a given organization. 

Based on these requirements, we defined the set of system capabilities needed to identify and 

locate that information. We tailored the information engineering process, consisting of the 

following steps, to reflect the nature of the UA. 

1. Define the Organization. We characterized the UA, including staff, suborganizations 

and significant variations. 

2. Acquire Knowledge. The research team identified organizational missions, tasks, 

functions, personnel, equipment types, capabilities, and conditions for employment, 

including tactical operations and command and control relationships. For the Future 

Force, this required the study of a range of documents that change frequently such as 

the O&O Plan, the ORD, the STRAP, and applicable white papers. In addition, we 

reviewed references applicable to similar organizations in the current or more nearterm force. These included the Army Universal Task List ([AUTL] TRADOC, 

1999), Combined Joint Task List ([CJTL] Chairman of the Joint Chiefs of Staff 

[CJCS], 2002), FM, and Mission Training Plans (MTP). Because many of these 

references have not yet been published for the Future Force, extrapolation from

current procedures and tasks provided a credible, “best available” set of unit tasks. 

3. Develop a Flowchart. We designed a detailed flowchart of activities proceeding from

organizational baseline specifications to compilation of a desired set of IR. Baseline 

points of derivation included structure, concept for use, performance tasks and 

internal and external relationships. 

4. Map Tasks to Functions. We selected specific organizational functions and mapped 

out tasks required to perform those functions. Various missions were evaluated in a 

16 
manner similar to the way the AUTL (TRADOC, 1999) maps tasks and sub-tasks. 

We selected the task of deploy to a tactical area of operations and tracked all the 

tasks and sub-tasks supporting the preparation, planning, deployment and 

employment of the UA. We built a matrix to organize the data, thereby facilitating 

subsequent retrieval and use. 

5. Synthesize Information Requirements. We developed an association from functions 

and tasks to applicable IRs. We noted probable sources of information to meet the 

IRs, if they were known. For each IR, we noted whether the information needs were 

to be shared with other units. 

6. Put Information Requirements into Categories. We analyzed the IRs and identified 

significant groupings or categories into which they could be sorted to expedite 

information source indexing. Some categories were very specific, such as “logistics,” 

“plans,” and “maneuver;” others were collapsed into a more general category such as 

“operations.” When the results were used to design an information gathering system, 

they were earmarked by group to determine sources of information or to express a 

capability requirement. 

7. Develop Search Queries. We developed a list of queries appropriate for use in the 

target information gathering system, which also resulted in a “key word” listing. 

8. Validate IRs and Queries. We validated the results by selecting representative 

samples of the IR lists across functional areas and conducting searches using a variety 

of search engines or a prototype advanced search tool if one was in production. For 

the Future Force, which will not be operational for ten years or so, such data did not 

exist. In this case representative or substitute information was developed for 

incorporation in prototype databases. 

9. Enumerate Required Capabilities. We converted the validated set of IRs into a list 

enumerating the capabilities required for a knowledge network intended to gather 

user requirements. Enumeration was done at several levels of detail. A more general 

enumeration identified capabilities at a higher level such as “Support Military 

Decision-making Process.” A more detailed list included specific needs such as 

“Retrieve all applicable mission OPLANS and OPORDS for higher, lower and lateral 

units.” 

The objective of the information engineering was to combine the entire organization, a 

suborganization, and the various individual staff members and commanders within that 

organization. The engineering process was used to identify the specific IRs, based on the 

mission tasks, operational concept, organizational structure, and functional relationships. 

Figure 4 diagrams how the data and information in these four areas flow into a series of 

analytical steps and ultimately a specified set of IRs. These activities identify common 

categories of information relative to a combat unit. Some modifications were made as applicable 

for maneuver support or other type units. Note that the process diagram accounts for selection of

a desired set of tasks or functions from which to derive IRs. A select cross-section of tasks was 

identified to use in the prototype to represent the overall unit. One aspect that must be 

considered when analyzing large organizations such as a brigade is that there are multiple 

17 
suborganizations. We had to choose whether to drill down to each level or to examine the 

organization from an aggregated perspective. In this project, the UA was analyzed as an 

aggregate for all functions with the exception of training. The figure illustrates this separation of 

the training function. Training was analyzed from brigade staff down to individual Soldiers. 

The other functions, such as Intelligence, were aggregated at the brigade staff level. 

Initially, the engineering effort addressed the unit IRs on a broad front, but eventually a 

set of battlefield functional areas (BFA), tasks, and activities were selected as indicated in the 

process schema (Figure 4). We examined all identified IRs to determine if there were any 

prevalent grouping based on source or similarity of activity. Two groups of IRs fell out as 

clearly significant: training and operations. Both of these areas have well established 

procedures and documentation that justify some form of special designation and treatment when 

designing information search tools. A third and more broad area of IRs, designated simply as 

“General,” covers all other information needs. These three IR types were identified to the 

technical developers of the knowledge network prototype as potential search index categories 

and search paths and for consideration in designing user interface windows (right side of the 

flowchart). 

Tasks Refs

- OF O&O

- UJTL

- AUTL

- MTPs

Operational

Concept

Functions/

Relationships

Organization

Structure

UA Brigade

Select

Tasks/

Activities

Select SubOrganizations

Conduct 

Task

Analysis/

- Task Decomposition

- Information needs 

relative to mission

- Multi- or cross functionality

Set of

BFA related

concepts

& functions

Define

Functional

Elements

IR

TYPE

Operational

General

IER Training

Command

Relationships

UA

Platforms

Training

Requirements

Ops

Nodes

Trainer

Nodes

Majority of taskswere

Operations or Training.

Tasks Refs

- OF O&O

- UJTL

- AUTL

- MTPs

Operational

Concept

Functions/

Relationships

Organization

Structure

UA Brigade

Select

Tasks/

Activities

Select SubOrganizations

Conduct 

Task

Analysis/

- Task Decomposition

- Information needs 

relative to mission

- Multi- or cross functionality

Set of

BFA related

concepts

& functions

Define

Functional

Elements

IR

TYPE

Operational

General

IER Training

Command

Relationships

UA

Platforms

Training

Requirements

Ops

Nodes

Trainer

Nodes

Majority of taskswere

Operations or Training.

Figure 4. Information engineering schema for prototype Knowledge Network. 

Scenario Driver 

As noted previously, the full set of tasks performed by a UA was pared down to a 

workable set that could be examined for mapping IRs to information network capabilities. A 

practical method to construct this mapping was to use a hypothetical set of conditions into which 

the unit was placed to stimulate various actions and reactions. The scenario selected for use in 

this research was based upon Future Force Mission Thread 1 (USAARMC, 2002a), similar to 

Future Force O&O Vignette 1 (Annex F) (USAARMC, 2003). The scenario envisioned the alert 

of a UA Brigade to deploy from its home station to a tactical operations area within 96 hours. 

18 
The use of a scenario driver in the information engineering process stimulated specificity 

in the tasks required to carry out the mission(s). Figure 5 graphically portrays how the process 

led to identification of IRs by type, the conversion of IRs to queries to be used on the knowledge 

network, and receipt of the search results. The process chart includes a subroutine that parses the 

IR into three basic categories of information (training, operations and general). It also includes 

the provision for sharing information with other members of the subject unit or other units. 

?

Query

USERS

Stimulate

Scenario

Driver

User element assigned

a mission by higher hqs.

What data/info

is required?

IRs Query

Query

Formulate Queries

Identify Sources

Results

?

Disposition

Discard

Use

Refer/Forward

Global Information

Grid [GIG]

Information 

needed or 

useful to others

Refer/Post to

Knowledge Net

Identify

Distinctive

IR Types

Develop

Representative

Samples

Of Each Type

?

Query

USERS

Stimulate

Scenario

Driver

User element assigned

a mission by higher hqs.

What data/info

is required?

IRs Query

Query

Formulate Queries

Identify Sources

Results

?

Disposition

Discard

Use

Refer/Forward

Global Information

Grid [GIG]

Information 

needed or 

useful to others

Refer/Post to

Knowledge Net

Identify

Distinctive

IR Types

Develop

Representative

Samples

Of Each Type

Figure 5. Information engineering—scenario driver. 

We used the scenario driver to identify select IRs and develop queries through the 

following five steps. 

1. We established a set of hypothetical users. 

2. We stimulated a given set of users with the introduction of a feasible scenario that 

activated staffs and commanders toward a set of plan, prepare, and execute actions. 

3. We developed a set of IRs based on analysis of the user unit mission and the data 

and/or information required to accomplish it. 

4. We discovered many pieces of information during the task analysis process that were 

of possible use to others. 

5. We classified IRs by distinctive types and probable sources. 

We began this process by interjecting scenario variables such as mission, region, and 

command and control (left side of Figure 5). We studied selected BFAs by functions that 

aligned with a set of tasks. In turn, we examined each task to extrapolate what information was 

19 
needed for its successful completion. Then we categorized the IR, after which each task was 

mapped to a stated query for the search tool, which in turn implied a required information 

gathering capability. These implied capabilities were synthesized and grouped prior to 

conversion to specific capabilities. The point of the capability mapping was to identify the 

specific capabilities needed to design a knowledge network for a UA. 

Unit of Action—Tasks and Functions Analysis. The UA command and staff functions 

were defined in terms of the basic organization and the BFAs that are foreseen for the Future 

Force. The Future Force O&O Plan for Maneuver UA (TRADOC, 2003b) provides some detail 

on how the UA staff and subordinate units are to be organized and equipped. The BFAs do not 

provide the level of specificity needed for a detailed analysis. A parallel analysis was made, 

therefore, using O&O and current staff subfunctions and the AUTL (TRADOC, 1999). One can 

use task lists such as the AUTL and Universal Joint Task List (UJTL) by taking a set of tasks, 

estimating the applicable function(s) required to perform them, and generalizing to analogous 

future tasks. 

 Functions Table. The baseline from which we derived the Future Force unit IRs 

consisted of a combination of functions and tasks from both the projected UA Brigade function 

and the way command and staff operates today. Table 2 illustrates this blend. 

The UA Brigade will operate in a joint environment. Joint interoperability requires that 

even functional tasks be translatable into common joint terms as expressed in the UJTL (CJCS, 

2002). Table 3 correlates the joint tasks to the Army BFA or concepts used in Table 2 

(TRADOC, 2003b). 

Research of current doctrine identified a relatively large number of sub-functional areas 

associated with the current AUTL and other formal task listings (left side of Table 2). The 

AUTL and MTPs do not yet exist for the UA. Given some modification and simplification of 

functions between current staffs and Future Force staffs, we found sufficient basis for 

determining IRs when using a specific scenario for employment of the UA. We constructed a 

matrix to facilitate the identification of IRs relative to specific BFAs and unit functions 

(illustrated in Figure 6). As depicted, the analysis can focus on one organizational level 

(brigade) or address multi-levels (brigade, battalion, company [USAARMC, 1998, 1999]). 

Capabilities Mapping 

Mapping is essentially a sub-process that parallels identification of information needs for 

a given organization, expressing IRs in terms of both general and specific individual or collective 

actions. For example, the general capability of the individual or staff to “plan” can also be 

expressed in a set of more specific capabilities (TRADOC, 1999), such as “to receive and 

distribute Operation Plans (OPLAN) and OPORD vertically and horizontally in the chain of

operational command” and “obtain higher headquarter Priority Intelligence Requirement (PIR)” 

(DA, 2001a). 

20 
Table 2 

Functions Baseline 

Current Areas and Functions Future Force Areas and Functions 

Battlefield

Functional Areas 

Staff Functions Battlefield

Functional Areas 

Staff Functions

Command and 

Control

Command Posts

Signal Support 

Plan and Prepare

Execute Operations

Maintain 

Battle Command Plans 

Synchronize Operations 

Track Battles 

Maintain COP 

Synchronize C4ISR 

Liaison 

Intelligence 

Communications 

Combat Intelligence 

Counter Intelligence 

Weather 

Psychological 

Operations (PSYOPS) 

Information Warfare 

ISR Manage Knowledge 

Network Base Functions 

Collect Intelligence 

Oversee All Intelligence 

Refine Threat Picture 

Analyze Battle Space 

Support Targeting 

Operations & 

Maneuver

Deploy

Ground Maneuver 

Aviation 

Security

Plan Operations

Plan electronic 

warfare 

Maneuver Track Operating Areas and 

Airspace 

Supervise Deployment 

Identify Maneuver Options 

Control Maneuvers 

Fire Support Precision Indirect 

Area Fires

General 

Attack Helicopter

Naval Gunfire 

Close Air 

Fires Select Targets 

Collaborate on intelligence 

 preparation of the 

 battlefield 

Direct Counter-Fire 

Clear Fires

Electronic Warfare 

Mobility & 

Survivability 

Air Defense 

Air Defense 

Nuclear, Biological and

Chemical (NBC) 

Combat Engineer 

Construction 

Bridging 

CMO 

Military Police 

Civil Affairs 

Pipeline 

Protection 

Maneuver

Support

Traffic 

CMO, Civil Affairs

Population Control 

Intra-Theater Movement 

Logistics 

Personnel

Personnel Management 

Medical 

Welfare/Morale

Public Affairs Office 

Chaplain 

Postal

Base Facilities & 

Utilities 

Recreation 

Graves Registration 

Finance & Legal 

Safety

Supply & 

Maintenance 

Transport 

Rear Operations

Water Purification 

Maneuver

Sustainment 

Support Plans 

Pulse Re-supply 

Provide Health Services 

Support Soldiers 

Manage Human Resources 

Provide Religious Support 

NA Training 

Schoolhouse 

Soldier Provide Training

Develop Leaders

21 
Table 3 

Correspondence of Unit of Action (UA) Tactical Concepts to Universal Joint Task List (UJTL) 

Tactical Tasks 

Tactical Concepts UJTL Tactical Tasks 

Battle Command TA 5 Exercise Command and Control 

Intelligence, Surveillance, and 

Reconnaissance (ISR) 

TA 2 Develop Intelligence 

Maneuver TA 1 Deploy/Conduct Maneuver 

Fires TA 3 Employ Firepower 

Maneuver Support TA 1 Deploy/Conduct Maneuver 

TA 6 Protect the Force 

TA 7 Operate in Chemical, Biological, Radiological, 

or Nuclear (CBRN) Environment 

Maneuver Sustainment TA 4 Perform Logistics and Combat Service Support 

BFA Integrated

Concepts Functions Tasks IRs Sources

BFA Integrated

Concepts Functions Tasks IRs Sources

BFA Integrated

Concepts Functions Tasks IRs Sources

USER

UNIT

INF & MCS CO

Task/Mission

Information

Needs

Scenario

CA Battalion

UA Brigade BFA Integrated

Concepts Functions Tasks IRs Sources

Battle

Command

ISR

Maneuver

Support

Maneuver

Sustain

Maneuver

FIRES

Battle

Command

ISR

Maneuver

Support

Maneuver

Sustain

Maneuver

FIRES

Multi-Level

Task/Mission

Information

Needs

One Unit Matrix

Multi-Unit Matrices V

A

L

I

D

A

T

E

BFA Integrated

Concepts Functions Tasks IRs Sources

BFA Integrated

Concepts Functions Tasks IRs Sources

BFA Integrated

Concepts Functions Tasks IRs Sources

USER

UNIT

INF & MCS CO

Task/Mission

Information

Needs

Scenario

CA Battalion

UA Brigade BFA Integrated

Concepts Functions Tasks IRs Sources

Battle

Command

ISR

Maneuver

Support

Maneuver

Sustain

Maneuver

FIRES

Battle

Command

ISR

Maneuver

Support

Maneuver

Sustain

Maneuver

FIRES

Multi-Level

Task/Mission

Information

Needs

One Unit Matrix

Multi-Unit Matrices V

A

L

I

D

A

T

E

Figure 6. Basic analytic framework. 

22 
Figure 7 carries the functions analysis process forward relative to a multilevel situation. 

This involves a grouping process that sorts BFA-derived tasks into recognized activity segments 

(right side) labeled as “High Level Network Capabilities Requirements.” Specificity is added to 

each high level capability when an IR is converted to a query (see Table 4). 

FIRES

TRAINING

MANEUVER

MANEUVER

SUSTAINMENT

MANEUVER

SUPPORT

ISR

BATTLE

COMMAND

FCS UA C4ISR System

F

C

S

l

CA

ll

UA

X

F

U

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O

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S

T

A

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K

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I

N

F

O

R

M

A

T

I

O

N

R

E

Q

U

I

R

E

M

E

N

T

S

T

N

G

STAFF

COLLECTIVE

INDIVIDUAL

GIG

HIGH-LEVEL

NETWORK

CAPABILITY

REQUIREMENTS

Plan

Train

Prep

Deploy Soldier

Rehearse Tac Ops

KNOWLEDGE

NETWORK

FIRES

TRAINING

MANEUVER

MANEUVER

SUSTAINMENT

MANEUVER

SUPPORT

ISR

BATTLE

COMMAND

FCS UA C4ISR System

F

C

S

l

CA

ll

UA

X

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U

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K

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I

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A

T

I

O

N

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E

Q

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E

M

E

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T

S

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G

STAFF

COLLECTIVE

INDIVIDUAL

GIG

HIGH-LEVEL

NETWORK

CAPABILITY

REQUIREMENTS

Plan

Train

Prep

Deploy Soldier

Rehearse Tac Ops

KNOWLEDGE

NETWORK

Figure 7. Capabilities mapping. 

Contextual application is significant relative to where tasks and the consequential IRs fit 

into the activity phases of an organization’s operations. The majority of staff functions and tasks 

could be sorted into six activity phases (DA, 2001a): (a) Train (in garrison, Combat Training 

Center [CTC], on-the-move and in-theater), (b) plan, (c) prepare, (d) deploy, (e) rehearse, and 

(f) conduct tactical operations. A seventh activity category, “Soldier,” was added to cover 

broader IRs that did not fit into any activity phase. The “Soldier” category is similar to “Train,” 

in that it is not necessarily associated with activities bounded by time. The fact that training, for 

both units and individuals, overlaps with the other five activities makes “train” a special case 

(TRADOC, 2003b). 

The six activity phases plus the special Soldier category were used to express the general 

capabilities required for any useful knowledge network for the UA. Each can be parsed into 

enabling or supporting capabilities. A partial mapping of general to specific capabilities is 

illustrated in Table 4. Included in this mapping is the inference of cross-communication and 

collaboration which will be greatly facilitated in the network-centric UA environment 

(TRADOC, 2002). 

23 
Table 4 

Capabilities Mapping—Partial Enumeration 

Major 

Activity Information Requirement Categories 

Plan Orders and plans from applicable units 

Map databases 

Logistics at staging area 

Intelligence summary on enemy or dissidents 

In-country infrastructure 

Prepare Classes of supply to be deployed with troops 

Personnel operational readiness and stop loss 

Regional geopolitical profiles 

Special clothing issue requirements 

Coalition force data 

Deploy Airlift schedules 

Locations of aerial ports of debarkation (APOD) and 

 sea ports of debarkation (SPOD) 

Immunizations required 

Media and Public Affairs (PA) releases 

Reception, Staging, Onward movement, and Integration plans 

Rehearse Embedded maps and graphics 

Air corridors and location of suppression of enemy air defense 

Graphics and 3-D visualizations 

Urban mock-ups 

Key decision points 

Tactical 

Operations 

Unit updates 

Shared data from ongoing contacts 

Barriers executed in sector 

Resources profiles 

Follow-on plans 

Train Crew drill and virtual gunnery TSPs 

Staff training

Lessons learned from past operations 

Crowd control and enemy prisoner of war operations 

Rules of engagement 

Soldier Promotion forecast 

Dependent dental and medical support 

Mailing address when deployed 

Health and welfare facilities 

Language training 

Prototype Development 

Software development began prior to conclusion of the front-end analysis phase. The 

results of the information engineering effort were made available to the prototype software 

engineers as they became available. Several key design concepts evolved based upon the results 

24 
of information engineering. It was clear that the following realities of UA operations and 

training must be accounted for in constructing a prototype knowledge network: 

• Compatibility. The prototype must be compatible with the Future Force C4ISR 

architecture to include access to a larger GIG (Department of Defense (DoD), 2002, 

2003). 

• Secure and Specialized Sources. Information requirements for a tactical maneuver 

unit will focus on operations and training. Information searches through knowledge 

networks for these two areas must be structured and should come from predictable or 

prescribed sources through dedicated search channels. 

• Training Support System. Training for FCS will be fully embedded and top-down 

supported, to include digital terrain databases (DA, 2002a). 

• HSOC. A designated HSOC will provide “reach” and support to deployed units, 

including facilitating communication from units to a full range of information 

resources in the Continental United States ([CONUS] DA, 2002b). 

• Functional Indexing. Individuals in certain functional positions within a unit will 

develop patterns for network use. For example, a Brigade intelligence functionary 

will develop a consistent pattern for searching operations sources, intelligence 

agencies and regional data sources. 

• Community Sites. Community sites will provide information on regions, countries, 

lessons learned, studies, historical profiles, and archived data. Some existing sites are

within the Army’s purview while others are Joint Service, DoD or Federal 

Government, e.g., Central Intelligence Agency, Department of State (DA, 2001b). 

• Open Web Sites. The broad range of information on the Worldwide Web must also 

be available to Soldiers searching through knowledge networks. 

The range of factors obtained through our research is graphically portrayed as a set of 

bands that characterize the knowledge network, first through the HSOC and then to widerranging sites and search channels. A narrow band controlled by the HSOC facilitates a focused 

search of a limited set of information that is highly relevant to current operations. Wider bands 

include additional information that covers a greater variety of topics from operational and 

collateral sources. The most general band provides access to the widest range of information 

from government, public, and private sources found on the GIG. Figure 8 portrays the three 

basic avenues of operational, training, and general information searches. To illustrate the 

concept of banding, a query traveling horizontally through the bands will be targeted initially at 

or through the close proximity sources or more dedicated sources. For example, the close range 

Operational band should successfully complete a query searching for the current JTF OPORD. 

A Civil Military Operations (CMO) planner might be required to do a more extensive search that 

goes from country specific information contained in the UE Division Intelligence Annex 

(Operational band) to specifics on the political military climate (General band). 

25 
Search(s)

Home-Station

Ops Center

(HSOC)

Operational

(Chain of Cmd

or Systemic)

Collateral

Dedicated Sites

GIG

General

TRAINING TSP/Manuals/CBT

Indiv/Collective

OPERATIONS OPLANS/OPORDS

INTSUMS/FRAGOS

GENERAL Lessons Learned/

Studies/Govt./

Regions/Political

KNOWLEDGE

NETWORK

PROTOTYPE

Information Sources

Search(s)

Home-Station

Ops Center

(HSOC)

Operational

(Chain of Cmd

or Systemic)

Collateral

Dedicated Sites

GIG

General

TRAINING TSP/Manuals/CBT

Indiv/Collective

OPERATIONS OPLANS/OPORDS

INTSUMS/FRAGOS

GENERAL Lessons Learned/

Studies/Govt./

Regions/Political

KNOWLEDGE

NETWORK

PROTOTYPE

Information Sources

Figure 8. Knowledge network banding effect. 

The technical aspects of building a prototype network were guided by the information 

engineering findings. We developed user interfaces with consideration for the factors discussed 

above. We refined the prototype incrementally by applying domain-specific capabilities 

highlighted by the research findings. The three search channels and the banding concept were 

integrated into the prototype. Use of the prototype required that a robust set of data and 

information files be created as credible search targets. We developed a set that was tailored to 

the scenario used for testing. The information package consisted of OPORD and plans (from

brigade through Joint Chiefs of Staff [JCS]), intelligence summaries (INTSUM), reports, 

lessons-learned reports, United Nations resolutions, country studies, rules of engagement, and 

data on weather, urban areas, population, infrastructure, and transportation. 

Prototype Description 

Knowledge Network System Overview 

The prototype KN provides several different methods to search for and distribute 

information. The goal of the prototype was to provide seamless searching for information from

different types of sources. These sources included Web-based documents, databases, news 

feeds, personal messages (e-mail), and files stored within various repositories. If the knowledge 

network is capable of searching these different areas in parallel, a user should be unaware of 

which search bands are being probed. Rather, the user should know only what results are 

returned. Different methods may be preferred under different circumstances. The prototype also 

allows a user to put information back into the network, thereby enabling the overall structure to 

grow over time. Once information has been incorporated into the structure it can be searched 

and viewed by others. 

26 
Main Page 

The prototype KN is a user-based system requiring a username and password to log on. 

Once a user logs on to the network, he arrives at a main page containing several different types 

of information (see Figure 9). The user may customize this information by selecting and/or 

hiding what categories should be displayed. The three main categories of information available 

are RSS feeds, blog messages, and quick links. 

1. The RSS feeds include information from news organizations or from local or higherlevel units. The user chooses which local and/or foreign news feeds to subscribe to in

order to gain a better understanding of the events taking place in a particular region or 

category. Unit feeds provide information from and about individuals within the unit, 

such as meeting times, distribution of manuals, or other unit-specific details. 

2. The blog allows users to post information that can be commented on by other users. 

The user can easily use a blog to obtain information on a particular topic by 

requesting for a comment on the topic of interest. An individual may have a question 

pertaining to a subject for which the answer may not be found on the Web or in other 

documents available to the user. The blog forum allows a user to post a question 

without the need to call around trying to track down someone who may have the 

answer. Blog messages also are a valuable method of distributing information within 

and among groups. 

3. Quick links allow a user to publish a listing of his most frequented sites. Placing 

these links on the main page permits a user direct access to a predetermined set of 

frequently used resources. Note that these links, as well as the feeds, are 

customizable to the particular user. Each user has the ability to control what 

information gets shown on his individual main page. 

Searching 

Searching is one of the primary components of knowledge networks. The prototype 

includes several methods for executing a search. Simple text-based searches are not necessarily 

the most straightforward method in all cases. Users unfamiliar with a topic may not know what 

keywords to enter (e.g., when looking for information on a particular region in a foreign 

country). Alternatively, users may know exactly what they are looking for and where it might be 

located, but they may need a means of finding it. The prototype network provides three methods 

of searching and locating information: 

• Full-text searching. 

• Concept-based searching.

• Fixed-document (menu-driven) searching. 

27 
Figure 9. Knowledge network - main page.

Each of these methods provides a user with a different means of obtaining information 

from the system. The type of information the user is searching for and how familiar he is with 

the subject influence what type of search he might use. Different users may also have a 

preference for a particular means of searching. For example, some users may prefer full-text 

searching capabilities that mimic those of GoogleTM and other familiar Web-based tools. Others 

may be less familiar with these tools and may prefer a more visual approach that allows them to 

drill down through a set of concepts they understand and can visualize. Once they reach a level 

in which the information they are after could be located, they can then execute a search and the 

documents related to that particular concept or set of concepts would be returned. Users who 

know exactly what they are looking for and basically where to find it may prefer a fixeddocument search in which they can select the specific details from a predefined list. 

Full-text searching. Because of its familiarity to most users, we believe that full-text 

searching will be a frequently used search method. Within the prototype knowledge network 

these are termed as general searches (shown in Figure 10) and simply allow a user to enter a set 

of keywords to search on. In addition, the user has the ability to select which information bands 

to search. This capability gives users more control over what types of documents get returned. 

For example, if the general band is selected, the user will likely get overview Web pages on a 

topic, whereas if the HSOC band is selected the user would get detailed training and FMs related 

to the types of missions his unit is likely to perform.

28 
Figure 10. General search. 

In general searches, users also have the ability to turn on and off various features of the 

system. By adjusting these features, the user can affect the priority of the documents that get 

returned (with higher priority documents being listed near the top of the list). When executing a 

search with the various features turned off, the system simply performs a Boolean-based search. 

All documents that match the query will get returned in the order that they are found, without 

ranking. There is thus no real way to evaluate the documents other than looking through each 

one by hand. The page-ranking feature allows for simple frequency counting within the 

documents to influence the ordering of how they get displayed. A document that has many more 

instances of the search terms will appear higher up than one that has fewer. 

A second feature of general searches that the user may select is user profiling. User 

profiling attempts to tailor information based on a user’s role or duty position. For example, an 

intelligence officer who executed a search of a particular country should expect to get 

information that is more related to terrain, enemy tactics, and so forth, whereas a CMO would 

get information more related to government and politics of the country. The profiling system

attempts to recommend information that is more related to the user’s role. Finally, users can 

select whether or not they want their search results to be placed back into the system and 

indexed. Placing search results back in the system allows future users to search another 

individual’s search results. 

Figure 11 shows the search results that were returned to a query executed on “Azerbaijan 

Transportation” with all optional features enabled. For each item returned, several types of 

information get displayed. The first item is the document name and a link to its location so the 

user can download and view it. The band in which the document originated is also given. This 

gives the user a better feel for where the information is coming from and allows him to evaluate 

in more detail what results may be most relevant to his needs. If user profiling is turned on, then 

29 
some additional information is also displayed. The profiler determines which category the 

document best fits into and then uses this to evaluate the information’s interest level to the user 

based on that individual’s role. In Figure 11, most of the results deal with transportation, as 

expected. The final column displays the document reliability. In the prototype, this reliability is 

determined based on the band in which the document appears. In an operational system this 

determination would be a far more complex process involving the source of the information, its 

age, and other factors. 

Figure 11. Search results. 

Through the use of a fairly complex algorithm (such as a rule-based system), it would be 

possible to estimate whether a given piece of information could be trusted or should be 

discarded. One possible extension to the current results displayed would be to add the ability for 

the user to sort based on the different columns. Currently, it sorts solely based on the document 

rankings. Adding the column-sort would allow a user to evaluate the information based on 

different perspectives. 

Concept-based searching. Concept-based searches allow for a more visual execution by 

allowing a user to drill down through a set of concepts. Although the prototype does not include 

30 
a working version of concept-based searching, it simulates how such a capability would work 

from the user’s viewpoint. Figure 12 shows a small representative sample of a concept map. In 

an actual search, this structure would be automatically generated through the use of clustering or 

some other data-mining technique. The user would first enter a starting term, for example 

“Azerbaijan” as in the example, and the system would generate and display the information in 

this type of format. The user could then click down through the structure. If the structure is 

large enough, the display may need to be multi-dimensional. A similar application is the engine 

developed by Semio used for the Center for Army Lessons Learned ([CALL] U.S. Army CALL, 

2003). 

Figure 12. Concept search. 

Two categories of concept searches were simulated in the prototype—a typical concept 

search and a search based on concept maps. Each of these works in a slightly different fashion. 

In the concept search, as a user is making a path down through the structure, the search engine 

tracks what items the user selects. When the user finally executes the search by pressing the 

button, the engine looks at the index and returns all documents that match the complete set of 

search terms the user selected. The concept map works slightly differently. The map never 

looks at the index when a search is executed. Rather, for each node in the graph there is a set of 

links to documents attached. For example, if a user were to select the geography node and then 

select search, a set of links involving maps and reports on the selected countries’ geography 

would be displayed. These documents would be linked beforehand, obviating the need to search 

the index. Documents can be linked in one of two ways: 

31 
1. Users can link documents to nodes as they come across them. 

2. Users may use data-mining/machine-learning algorithms to automatically generate 

the structure and link items to their proper nodes. 

Menu-based searching. The third category is a menu-based search (shown in Figure 13). 

A menu search allows a user to locate a particular document that he knows exists. For example, 

if a user were seeking a document on virtual training environment related to urban terrain and 

knew the document had been posted, a menu search would enable him to locate and access it 

simply. The user would select the training tab, followed by the collective training tab, and then 

select the unit type and desired echelon. The search engine would present all documents 

matching the search, permitting the user to page through and find the desired document. 

Similarly, a user could look up information with respect to operations or regional data. Several 

additional subcategories are listed below each element. 

Figure 13. Menu search. 

Generic Web-based searching. In some cases user may be looking for general 

information located only on the Internet (an unconstrained search in the last band). To access 

this information, the prototype provides users with the ability to conduce Web-based searches 

(Figure 14) directly, using some of the most popular commercial search engines available on the 

Web. The user enters the search terms in the specified box, selects the engine he wishes to use, 

and hits the search button; he will be automatically transported to the engine and the search will 

get executed.

32 
Figure 14. Generic Web search. 

Adding Information to the Network 

Aside from searching, the user can also put information back into the system through one 

of several different methods. These include: 

• Subscribing to different news/unit feeds. 

• Posting or replying to blog messages. 

• Sending or replying to personal messages. 

• Posting documents into the local or unit’s repository. 

Information put back into the system can be incorporated into the index (once the index 

has been rebuilt) and is fully searchable by all users. Every time a user updates his news/unit 

feeds, posts a message to a blog, sends a personal message, or loads a document into the 

repository, the information is copied into the cache of documents that are used to build the index. 

Attached to each document is also metadata that stores where the object came from and its band. 

As more information gets loaded into the system over time, it will become more comprehensive. 

The ability to put information back into the system to enlarge and improve the available data is 

very important to the concept of knowledge networks. Through information sharing and 

growing the overall structure, the network is made more usable and more powerful. 

Implementation Details 

The overall prototype knowledge network uses the Zope Content Management 

Framework ([CMF] Zope Corp., 2003). Zope is a framework for building custom Web-based 

applications. Zope is supported by a large community of developers who have created (and 

33 
maintain) many different modules. Because these modules are available, a site can be more 

quickly developed by facilitating code reuse. Zope is built in the Python programming language 

and supports scripting, HTML, and dynamic template markup language ([DTML], a Zopespecific language for generating more dynamic pages) development within its framework. The 

prototype knowledge network uses a combination of Python scripts, HTML, DTML, and both 

Java and C code. 

The prototype framework is divided into several modules (shown as boxes in Figure 15). 

The components include: 

• Crawler. 

• Searches. 

• Index. 

• Blog. 

• RSS Feeds. 

• Personal Messages. 

• Repository. 

• System File Import/Export. 

• User Profiler. 

Crawler Searches

Index

Repository

RSS Feeds

System File

Importer/

Exporter

User

Profiler

Blog

Zope

C Code

Personal

Messages

Crawler Searches

Index

Repository

RSS Feeds

System File

Importer/

Exporter

User

Profiler

Blog

Zope

C Code

Personal

Messages

Figure 15. Knowledge network framework. 

34 
 Crawler. A crawler works in a fairly simple manner. Within the prototype knowledge 

network, a page under the crawler folder allows a user to submit a URL for crawling. Once the 

user presses the submit button, the crawler begins to retrieve documents for indexing. The user 

can specify how many levels the crawler will go before stopping. We recommend that users 

strictly limit the number of search levels, because the greater the number of levels, the more 

information is brought into the pipeline. The Web hierarchy is a tree-like structure and the 

volume of information increases greatly with each level added. 

This fairly simple method used for the prototype could easily be extended to store URLs 

in a database and direct the crawler to periodically browse the URLs for new updates at the 

various sources. The crawler is based on the common UNIX utility wget (GNU Project, 2002). 

Wget is a freely available software package that allows for retrieving files using one of several 

protocols. Wget has multiple features that allow for transferring large files as well as mirroring 

sites. 

Blog (Weblog). The blog is based on the very popular Zope product, Squishdot 

(Landingin & Withers, 2003). Squishdot is freely available open-source software that allows for 

the publishing and discussion of information through the use of a Weblog structure. The generic 

Squishdot framework is integrated into the prototype system. The prototype allows the user to 

view and post messages as well as to comment on previously posted messages. Document 

attachments are also allowed, so a user could attach a manual or digital overlay for others to 

evaluate and provide feedback. All information posted to the blog will also be included into the 

index and will be searchable once the index has been regenerated. 

Rich Site Summary (RSS) Feeds. This section of the prototype network is built from the 

Zope Resource Description Framework (RDF) Summary module (Zope Corp., 2003). This 

module allows a user to specify the URL where a particular feed is distributed and upon request 

will connect to the site and capture the feed, process it and store the information so it can be 

displayed and used as needed. This module is integrated into the knowledge network and each 

role has a set of associated feeds. In actual use, it would be best for each user to be able to 

control what feeds would be displayed on the main page. This degree of customization would be 

possible by providing a master list of all available feeds accessible by the network to its 

subscribers. The feeds can be updated in one of two ways: a button appears on the page that 

allows the user to select when the feeds get updated (how the prototype functions) or a timer 

could be used to determine when the feeds are to be automatically updated. A combination of 

the two, providing for automatic updating, but permitting the user control over when to receive 

the updates would be the best solution. The time the feed was last updated gets displayed along 

with the information so the user can easily determine its currency. 

 Index. The index is also built from a series of Zope modules. For information on 

searching and cataloging content within Zope see The Zope Book, (2003). All documents 

incorporated into the index are stemmed and the stop words are removed. The indexes are 

compressed in order to reduce storage space and speed up the searching process. The index for 

the prototype system is broken into two separate indexes that can be searched in parallel. The 

first index contains all of the documents collected by the crawler. The second index contains all 

35 
the remaining documents collected from sources such as repositories, personal messages, feeds, 

and blogs.

Separating the indexes has several benefits. First, the indexes can then be regenerated 

independently. This allows an index that requires frequent updating to be regenerated without 

the time burden associated with regenerating the entire large index based on documents collected 

by the crawler. Secondly, it allows for more control when performing a search. If a user is not 

interested in the information gathered by the crawler, he can limit his search to the index that 

contains information from sources other than the crawler. The indexes could be split into more 

than just the two demonstrated in the prototype, providing users with even more control over 

where they choose to search and thereby speeding up the overall process by reducing the number 

of matching documents returned after executing a search.

Search Methods. The search methods used in the prototype consist of a combination of 

Python scripts and Java code. Zope provides several methods for accessing the indexes. We

extended these methods greatly in order to achieve the desired level of functionality. 

Additionally, we attached metadata to all documents to enable the system to search based on the 

bands and to estimate a document’s reliability. When a user selects the bands in which he is 

interested, the metadata are analyzed and only information from those bands matching the search 

parameters will get returned.

The general search uses a set of methods written in Python. If user profiling is turned on, 

the resulting document list from a search is passed to the profiler. The profiler returns a set of

newly ordered documents based on an estimate of the interest levels of the user.

The concept searches are implemented as a Java applet (a small program that can be 

embedded in an HTML page). The applet tracks the items a user selects and makes a call to the 

appropriate general search function, with the user selection passed as a parameter of the call. 

The fixed-document searches use DTML and Python scripts. Each menu item is mapped

to a folder in which the relevant documents are placed. When a user executes a search, all 

documents stored in a particular folder get returned to the user.

Finally, the Web-based search simply takes a set of search terms from the user, formats a 

search string for the particular search engine of choice, and calls the engine with the search 

string. The user is then transferred to the results page for that engine. The search module is by 

far the most complicated in the prototype and integrates several different components to achieve 

functionality.

Personal Messages. Personal messages within the prototype use a simple Internet 

Message Access Protocol (IMAP) client for mail storage and retrieval with one extension. The 

messages that are sent get incorporated into the overall structure of information made available 

to the user. Much of the information communicated between individuals may be potentially 

useful to the user community at large. This information is made available by incorporation into 

the index. These messages are not intended to be used as normal communication between 

individuals, but rather as a source for questions and answers and the communication of important 

36 
information within the framework. If such a method were to be used outside of the prototype, 

users would likely demand more control over whether their mail were placed into the index. 

System File Import/Export. This tool was implemented in Python to allow documents to 

be brought into the prototype network easily. This module can be found under the Menu/Import 

folder. A user selects the document to import, identifies the location to place it, and selects the 

band with which the document should be associated. This interface is not intended for general 

users but is focused towards the administrators running the system to allow them to bring 

documents into the system quickly. 

User Profiler. The user profiler attempts to place documents into their best-suited 

category. Currently, the following nine categories are used: Communications, culture, 

economy, environment, government, health, military, security, and transportation. This list could 

easily be extended to include many additional items. In general, the set of categories should be 

tailored to support the appropriate application domain. 

The user profiler integrates several different technologies to assist users in finding 

interesting and relevant information more quickly. Each user has an associated role and each 

role has a customizable set of categories associated with it. By evaluating to what degree a 

document fits into these categories, a document can be assessed a predicted interest level for a 

particular user. This assessment is broken into five interest levels: very interesting, interesting, 

neutral, not interesting, and not interesting at all. A user’s profile (based on his role) is updated 

every time a query is executed, so that, over time, it will better learn what type of information he 

prefers. The prototype system filters this information through the use of a series of algorithms. 

These include: (a) a category trainer, which trains each category to identify documents that fit 

into it by evaluating a set of collected data specific to that category; (b) the document classifier, 

which fits a document into one of several different categories; and (c) the user’s interest 

evaluator, which, for a given document and associated category, determines a user’s interest

levels for a particular document. The profiler uses k-means clustering to classify user interests 

and a simple Euclidean distance to measure the similarity of a matched document to a particular 

user’s interests. K-means clustering is ideally suited for this application in that it generates a 

specific number of disjoint, flat (non-hierarchical) clusters and produces a numerical solution in 

an unsupervised environment. 

These modules (as shown in Figure 15) are integrated into a common framework that 

forms the prototype system. The framework has been extended by adding features such as the 

ability to store previously executed searches. The overall system, developed solely as a 

prototype, has limitations regarding the local data available to it, the reliance on freely available 

tools that do not offer the capabilities of commercial tools, and the incomplete implementation of

some capabilities, such as concept searching. However, at the current level of development the 

prototype provides a test environment for exploring technologies related to information retrieval, 

indexing, searching, and user profiling and for determining how they may be used to support IRs 

for the Future Force. 

37 
Knowledge Network System Innovations 

The prototype knowledge network incorporates several innovations resulting from the 

integration of different technologies within a single framework. The prototype provides the user 

more control over the information obtained from the system, compared to existing search engines 

and related tools, as well as the ability to easily distribute and make new information available to 

other users on the network. Information acquisition and distribution are thus integrated into a 

single, consolidated framework. The prototype also allows a more dynamic exchange of 

information within the system—an exchange that goes beyond that which standard search 

engines provide today. 

The control provided to the user by the KN over how to search information can be 

viewed as the primary benefit of this tool. By allowing users to select from different search 

methods depending on the subject and their knowledge of what they are looking for, the system

allows them to locate the desired information more easily. Additionally, by allowing control 

over where information comes from within the bands, users can reduce the overall number of 

documents they have to review to obtain the sought after information. 

A second benefit of the prototype is that it allows for a more productive and usable 

system by integrating methods to share information with other users of the network. This 

capability can expand the impact that information has, by allowing it to be shared among the 

members of the UA, or between the UA and other units. 

The third notable benefit is the concept of seamless information searching. Within this 

network, users could simultaneously search information stored within different indexes and 

databases, whether on the Web or in local repositories. By allowing users more control over the 

source of the information they retrieve, the system becomes a more useful and valuable tool. 

This combination of being able to control where the information is coming from and to use 

different means of searching for the same data makes the prototype a more flexible system. 

Knowledge Network Prototype Demonstration 

A demonstration of the prototype knowledge network was conducted at Fort Knox to 

generate feedback and provide the basis for adjustments in design and capabilities prior to 

project completion. 

Participants 

Army Research Institute (ARI) personnel at Fort Knox arranged for four Soldiers from 

the Unit of Action Maneuver Battle Lab (UAMBL) to participate in the demonstration. Two 

were commissioned officers and two were NCOs. A separate demonstration was conducted for 

each participant. 

The participants differed in their knowledge of Future Force concepts and of battlefield 

automation systems. Both of the officers were assigned to the UAMBL, and consequently were 

relatively familiar with the capabilities and other characteristics of the Future Force, as well as 

38 
the relevant requirements documentation. In addition, one had a background in information 

technology, while the other had experience as an intelligence officer. 

The NCOs had considerable (15–20 years) Army experience, but little knowledge of 

Future Force requirements. In addition, one had little experience with digital versions of weapon 

systems, such as the M1A2 or the System Enhancement Program versions. 

Method 

Demonstration scenario. A specific mission thread was selected as the foundation for the 

scenario used to demonstrate the prototype network. The selected thread was “UA Brigade 

conducts operational maneuver over strategic distances to an austere theater into multiple 

locations” (USAARMC, 2002a). With this thread as the anchor point for analysis and scenario 

and vignette development, the demonstration participants were instructed to consider the actions 

of the UA staff and commanders that would be conducted at each of the following three times. 

1. From receipt of the Alert Order through deployment from an aerial port of 

embarkation to arrival at an aerial port of debarkation (APOD); 

2. Subsequent in-theater actions relative to reception, staging, onward movement and 

integration; and 

3. During preparation for onward movement. 

A realistic scenario for a UA operating in 2015 was developed to provide information for 

prototype development and demonstration. The vignette was based on Vignette 1 (Annex F) of 

the O&O Plan (TRADOC, 2003b). In the vignette, a UA is alerted and then ordered to deploy as 

part of a UE Division to the country of Azerbaijan to conduct stability and peacekeeping 

operations in an unspecified sector. 

 Procedure. The demonstration allowed each of the four participants to role-play as an

ISR officer, a CMO officer, or as an NCO in these functional areas. These areas present two 

different perspectives on the types of IRs that would be required to complete the mission. On a 

concept basis, the more open and unrestricted of these areas is CMO because the IRs deal with a 

broad range of topics from regional terrain to civilian authorities and infrastructure. Intelligence

addresses a balance of closed net classified data retrieval, as well as open terrain and climatic 

data. We believed that the two roles considered in the demonstration provided an environment in 

which we could adequately demonstrate all features of the prototype. 

At the beginning of the demonstration, the participants reviewed written material that

described the scenario and summarized the road to war. The description included a description 

of the relevant geographical, political, and military events that had occurred before the beginning 

of the scenario. It then summarized the mission and provided orders and other related material. 

After the participant read the material, we answered any questions that he had about the mission. 

The participants then received an overview of the design goals and capabilities of the 

prototype KN and a quick tour of system functions. The overview described the general 

39 
capabilities of the system, illustrated how the system was controlled, and gave some examples of

specific functions, such as searches or RSS news feeds. 

Participants then began the hands-on role-playing exercise using the system to identify 

specific information required for either Intelligence or CMO. In general, we prompted the user 

with an IR, which he then tried to satisfy using the prototype KN. We gave the participant as 

little help as possible, but did provide assistance in operating the system when it was needed. 

Also, we sometimes suggested that the participant use specific capabilities, to help ensure that all 

of the functions were covered. For example, we encouraged the participant to use all types of

searches. The demonstration continued until all of the features of the prototype were covered 

and the participant had no further comments. Time for the demonstration varied between one 

and two hours. 

The demonstration was conducted on a laptop with a wireless connection to the Web 

server. Observers watched a second display that was connected to the laptop and took notes of 

participant comments. 

Results 

The primary results consist of the comments of the four participants in the demonstration. 

Although it is difficult to generalize from such a small sample, the reactions of the participants 

did provide useful information, which provided the basis for several enhancements to the 

prototype software. We organize the results from these comments by prototype function. 

Searching. The participants tried all three search methods (text, concept, and menu 

search). Their preferences differed, especially with regard to concept searches. One of the 

officers preferred the text search to the concept search, in part because it supported a general 

search strategy that allowed him to review a list of matches to identify the relevant material. 

However, the second officer preferred the concept search, because he was not happy with 

previous experience with text-searching capabilities of commercial search engines such as 

GoogleTM. One of the NCOs was favorably impressed with concept searching, as well. 

The participants saw different uses for the different search methods. For example, one 

officer used the menu search capability for finding formal documents, such as training manuals 

or other official publications. The organization of the menus allowed him to locate the desired 

information quickly. A text or concept search, on the other hand, would be more appropriate for 

more open-ended searches. 

Participants saw benefits to the banding concept in identifying the most relevant search 

results. One of the NCOs indicated that it was already difficult to find documents, such as forms,

on Army Knowledge Online (AKO), because of the wealth of information accessible from that 

site. The other NCO stated that the HSOC would be the primary source of information for the 

vast majority of IRs, implying that banding would be a very effective strategy. 

Sharing information. One of the officers stated that blogs could provide useful 

information to a unit that is preparing to deploy, including environmental conditions, special 

40 
requirements, need for special equipment, and other information. An NCO echoed this opinion 

and added that blogs could provide information on enemy capabilities, weapon types, and other 

enemy characteristics. In a related topic, one of the officers suggested that it should be possible 

to forward the results of a search to others who may also need that information. 

Display features. Participants had a variety of suggestions for new display and control 

features. These suggestions are enumerated in the following list. 

• Use more hot buttons rather than fill-in menus to initiate common searches; 

• Add audio and visual alerts for time-critical information; 

• Provide additional main-page information including some form of “ticker tape” for 

information such as weather information or alerts; 

• Use key words or buttons to access training documents such as TSPs; and 

• Make key information extracts available in bulletin board format (e.g., rules of 

engagement, hazardous conditions). 

Integration. Participants expressed concern that the knowledge network provide a 

consistent interface in different situations, and that it should be consistent with other systems, 

from the user’s perspective. One officer stressed that the same interface should be useable both 

in garrison and on deployment. An NCO expressed the opinion that the display should be 

consistent with other vehicle displays to the extent feasible. Finally, they suggested that it would 

be useful to make the information portable by integrating the network with personal digital 

assistants (PDAs). 

Other comments. One of the participants suggested that different information should be 

available at different echelons. For example, news feeds would be most useful at higher levels 

(e.g., for the UA staff). In addition, participants made several specific suggestions about 

terminology, output details, and specific options included in the prototype. 

During the demonstration, the participants mentioned several times that users would want 

control over access to information. This level of control goes beyond simple password 

protection of pages. The desired capability would allow users to define groups and to specify 

which groups have access to a particular piece of information, thus allowing users more control 

over the distribution of information. Limiting access would enhance security, reduce the 

likelihood of information misuse, and protect against inappropriate use of draft documents. 

Interpretation of Results

The results of the demonstration should be interpreted in light of the preliminary nature 

of the prototype. Specifically, the prototype was developed over a short time, and thus many of 

the features (e.g., concept searching) were incomplete. Additional limitations of the 

demonstration result from the fact that it was conducted 12 years in advance of the period that it 

was emulating. Therefore, the range of the search activities was limited for the training and 

operations channels. 

41 
Further development of the system would complete the representation of existing 

capabilities and provide additional features. These features would provide better control over the 

information that gets displayed (e.g., controlling what feeds, links, and blogs can be displayed on 

the homepage) and might be tailored to the user’s role. 

Nevertheless, the reaction of the participants was generally positive, and they clearly 

perceived the usefulness of most of the features of the prototype. Their suggestions provided 

guidance for refining the system features and incorporating new capabilities. 

Discussion 

The Future Force will operate in an information-rich environment in which understanding 

the implications of available information and using this understanding to construct appropriate 

action plans will be required for victory. Soldiers and units in the Future Force will benefit from

methods to retrieve and organize information efficiently and to share it with others who may 

need it. The research described in this report developed a prototype network integrating several 

information storage and retrieval methods that could be used to support Soldiers and units in the 

Future Force. The prototype was developed using freely available tools, and a combination of 

developed and open source components. We demonstrated the prototype to several Army

officers and NCOs, who provided their opinions and impressions of both the functional nature of 

the tool and its usefulness within their current activities. The overall user feedback was positive 

and provided guidance for system revisions and enhancements. 

The prototype was based on reviews and analyses of Future Force IRs and technologies 

used for information search, retrieval, storage, and sharing. The review of IRs identified the 

types of information that would be needed to support the operations and training of Future Force 

units. The results of this analysis were used to define the required capabilities of the knowledge 

network. In a similar fashion, the analysis of technology focused on existing technology that had 

the potential to meet some or all of the needs of the Future Force. Although the capabilities that 

were incorporated into the prototype KN already existed in some form, they continue to be 

enhanced to improve their performance. Consequently, future systems that take advantage of 

enhanced capabilities of the technologies should provide additional benefits to users. 

The prototype network integrated a variety of methods to search and retrieve information, 

to receive updates about ongoing information needs, and to share information with others. The 

methods were designed to provide the Soldier with needed knowledge quickly and efficiently, 

and to minimize the amount of extraneous data that would get in the way. Differences in user 

information needs and preferences were accommodated by providing multiple search methods 

and by user profiling. The ability to update repositories was provided through messaging 

services, web logs, and file posting utilities. Although the databases were necessarily incomplete 

and could not include the actual documents that would be available a decade from now, the 

functions were represented by working versions, rather than display mockups or storyboards. 

Despite limits in the extent of the prototype demonstration, it illustrated the functions 

performed by knowledge networks, provided a way to obtain the impressions of potential users, 

and generated a list of suggestions for improved system functions. The positive response speaks 

both to the recognized need for the capabilities and the ability of the functions included in the 

42 
prototype to meet that need. Obviously, more definitive conclusions would require both a larger 

sample and a more rigorous evaluation plan. 

We believe that the capabilities of the prototype and the results of the demonstration 

justify further efforts to develop knowledge network capabilities and to integrate these 

capabilities into Future Force C4ISR networks. We continue this discussion by addressing some

issues related to implementation. Then we discuss the role that information engineering played 

in the development of the prototype and its importance in the development of operational 

knowledge networks. Finally, we briefly list several attractive prospects for future research and 

development efforts. 

Integration of Knowledge Networks into Future Force C4ISR 

We believe that the kinds of features that were included in the prototype will provide 

essential capabilities for UAs to perform their missions. The need for these capabilities is clear, 

and the prototype has demonstrated the feasibility of existing technology to meet the need, 

although further development is undoubtedly required in some areas. Further development and 

analyses will be required to establish the utility of knowledge network technologies. In addition, 

development must address several implementation issues. 

Need for Capabilities 

The explosion of information available to Soldiers and their units is certain to continue to 

affect the Future Force. The Army plans that FCS-equipped units will be networked horizontally 

and vertically at all levels (TRADOC, 2003b). In this environment, fast, smart and efficient 

searching techniques that can be engineered to meet the training and operational needs of a unit 

will be vital to the UA to ensure that it meets goals for adaptability and flexibility. 

Feasibility and Acceptability of Technology 

The prototype knowledge network demonstrates the feasibility of many of the 

technologies to support operations and training in a limited information environment. It should 

be pointed out that the freely available tools that were used for the prototype are not as capable 

as commercially available tools. In addition, most of the tools, such as indexing and search 

algorithms and user profiling methods, are the subject of considerable ongoing research, so that 

future systems will be much more capable. Consequently, it seems reasonable to conclude that 

with the normal progress of information technology, it will be possible to meet the information 

needs of the Future Force. 

Utility of Technology 

This research did not evaluate the utility of the technologies that were incorporated into 

the prototype to support better or quicker decisions. Nevertheless, it seems to be an intriguing 

possibility worthy of further study that some of these technologies can improve decision speed or 

quality by enhancing the performance of certain elements of the decision-making cycle. 

43 
The decision-making cycle describes a process that begins with sensing relevant 

information on a situation, and continues through activities that process the information to better 

understand the situation in order to make a decision that is converted into action. The impact of 

the knowledge network on the decision-making cycle is best illustrated using the cognitive 

hierarchy first described in Figure 2. It seems possible that a properly designed network could 

provide information to Soldiers that would be more readily usable by them, in effect bypassing 

or shortening some of the processes required to obtain and assimilate information. The potential 

improvements in the decision-making cycle are illustrated in Figure 16 in the context of the 

cognitive hierarchy. On the right side of the figure, initial processing is compressed and the 

overall cycle time is reduced. 

The potential improvements in the decision-making cycle are in no way guaranteed, and 

undoubtedly depend on many design and implementation factors, including the following: 

• Comprehensive information engineering, 

• Fast, smart, cognitively adjustable search engines, 

• Differentiation between data and processed data sources, 

• Tools that provide incentives for information sharing and collaboration, and 

• Interoperability between networks and battle command system tools for problem

solving and decision aids. 

Data

Understanding

Knowledge

Judgment

Cognition

Processing

Information

Data

Knowledge

Judgment

Cognition

Information

Processing

T

i

m

e

Decision

Action

Decision

Action

T

i

m

e

GLOBAL INFORMATION ENVIRONMENT

Current Deliberate Process

Network Assisted Process

Collaboration

Understanding

Data

Understanding

Knowledge

Judgment

Cognition

Processing

Information

Data

Knowledge

Judgment

Cognition

Information

Processing

T

i

m

e

Decision

Action

Decision

Action

T

i

m

e

GLOBAL INFORMATION ENVIRONMENT

Current Deliberate Process

Network Assisted Process

Collaboration

Understanding

Figure 16. Potential reduction of time requirement for decision cycle. 

44 
Although these improvements have not been demonstrated, they should be incorporated as 

development goals and evaluation criteria for future development efforts. 

Implementation Issues 

Future knowledge network development should build upon the developments of other 

programs under construction for Future Force use. Such programs include the Army Knowledge 

Management (AKM) and Army Knowledge Enterprise (AKE) programs, as well as the 

information and methods included in the U.S. Army CALL Web site. In the training arena, the 

Army Training Support Center (ATSC) is sponsoring a project to develop an Army Training 

Information Architecture–Migrated (ATIA-M). This program is intended to provide a suite of 

Web-based training support applications. 

Implementation of knowledge networks must allow UA and UE units to be interoperable 

from Army to Joint, Interagency and even Multi-National levels. Although information 

exchange requirements are not quantifiable at this time, it can be assumed that at a minimum the 

networks must provide for joint information distribution and exchange. 

Importance of Information Engineering in Knowledge Network Development 

Information engineering was an important component in the specification, design, 

implementation, and demonstration of the prototype knowledge network. We believe that 

successful implementation of an operational network will demand a thorough information 

engineering process that can account for unanticipated IRs, because of the uncertainty of future 

mission requirements. 

Organization of Information Requirements 

The primary outcomes from the information engineering process are the identification

and organization of IRs, which are the basis of system requirements. Information requirements 

are organized by activity phase to specify the information demand sequence and frequency of 

need. The results of information engineering for the UA organize IRs into three categories, 

training, operations, and a third more general category covering a variety of other needs. The 

results of this engineering effort specify required knowledge network capabilities to support UA 

activities to train, plan, prepare, deploy, rehearse, conduct tactical operations, and support the 

Soldier. 

User Interface 

Details of the user interface were not the primary focus of this research. Nevertheless, 

results of the prototype demonstration indicated the utility of improvements in the user interface 

design and implementation. For example, the participants in the demonstration indicated that 

direct links should be provided to specific documents that a user would be likely to search for, 

such as FMs and TSPs. In addition, links to information from a common source, such as the 

HSOC, can also facilitate its use. Such links could provide easy access to plans, guidance, and 

orders. Other suggestions from demonstration participants also addressed user interface issues, 

45 
including integrating the searching interface with the battle command system, providing audible 

or visual alerts for information updates, and download of personal items to portable digital 

assistants. These and other user interface requirements should be identified as a result of the 

information engineering process. 

Research and Development Needs 

The results of the development effort show the potential benefits of technologies for 

knowledge networks, but also indicate the additional work that will be required to implement 

them. We briefly list areas where such research is needed. 

1. Enumeration of information needs and sources. Information engineering should 

continue to determine requirements for more complete knowledge networks. The 

analysis should envision the wide range of activities that will be conducted and 

trained by Future Force units. 

2. Testing concepts for use of features in simulated environments. Some of the concepts 

developed in the prototype need to be tested in more realistic environments to 

discover how they might be used and to determine their utility. For example, 

although blogs were included in the prototype, we did not develop a detailed concept 

of operations for this feature. Further research is needed to identify potential methods 

for using features such as blogs, and to anticipate the potential benefits and pitfalls of

these methods. The development work should be followed by empirical evaluations 

in which they are used in a simulated mission with realistic level of complexity and 

IRs. 

3. Testing and optimizing organizational constructs. We have specified fairly simple 

methods of banding and user profiling to organize information for efficient retrieval. 

Future research should attempt to optimize these methods, producing, for example, 

the best set of rules for defining the information bands. Evaluation of improved 

organizational constructs can combine analytical and empirical methods. For 

example, it may be possible to analyze the content of IRs to derive analytically the 

optimal number of bands or the distribution of information across bands. The results 

of the analytical evaluation would then be confirmed by an empirical evaluation in a 

realistic mission scenario. 

4. Prototype enhancements. There are many enhancements possible to the components 

of the prototype knowledge network. The enhancement process should first address 

those components in which it is judged that substantial benefits can be obtained 

through a minimum development effort.

5. Overall prototype evaluation. Some of the most interesting questions about the 

effectiveness of knowledge networks, such as the effect of their use on the decision 

cycle, must await the development of complete prototype that operates in a realistic 

information environment. A variety of study designs can provide specific guidance to 

issues in the development and implementation of knowledge networks.

In the Future Force, information will be more plentiful and its control will be more 

important to mission accomplishment than it is today. The availability of tools that facilitate the 

46 
construction and sharing of knowledge based on the available information will enhance the 

capabilities of the Future Force. The prototype knowledge network developed for this project 

illustrates some of the capabilities that could be able to enhance the performance of UAs. The 

results of this research can be used to guide future research and development efforts to provide 

operational knowledge networks for the Future Force. 

47 

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51 
Appendix A 

Acronyms 

ABLE Agent Building and Learning Environment 

AKE Army Knowledge Enterprise 

AKM Army Knowledge Management 

AKO Army Knowledge Online 

APOD Aerial Ports of Debarkation 

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

ATIA-M Army Training Information Architecture–Migrated 

ATSC Army Training Support Center 

AUTL Army Universal Task List 

BCOTM Battle Command on the Move 

BFA Battlefield Functional Areas 

C4ISR Command, Control, Communications, Computers, Intelligence, Surveillance, 

and Reconnaissance 

CALL Center for Army Lessons Learned 

CBRN Chemical, Biological, Radiological, or Nuclear 

CBT Computer-based Training 

CFX Command Field Exercise 

CGF Computer Generated Forces 

CIAgent Constructing Intelligent Agents 

CIS Command Information System

CJCS Chairman of the Joint Chiefs of Staff 

CJTL Combined Joint Task List 

CMF Content Management Framework 

CMO Civil Military Operations

CONUS Continental United States

COP Common Operational Picture 

CPX Command Post Exercise 

CTC Combat Training Center 

DA Department of the Army

DIDb Distributed Information Database 

DTML Dynamic Template Markup Language 

FCS Future Combat Systems 

FM Field Manual 

FRAGO Fragmentary Order 

GIG Global Information Grid 

HSOC Home Station Operations Center 

A-1 
HTML Hypertext Markup Language 

IMAP Internet Message Access Protocol 

INTSUM Intelligence Summary 

IR Information Requirement 

IS Information Superiority 

ISR Intelligence, Surveillance, and Reconnaissance 

JCS Joint Chiefs of Staff

JTF Joint Task Force 

KN Knowledge Networks 

MCO Major Combat Operations 

METL Mission Essential Task List 

MTP Mission Training Plan 

NBC Nuclear, Biological and Chemical 

NCA National Command Authority 

NCO Noncommissioned Officer 

NRFTT Networkable, Reconfigurable, Full-Task Trainer 

O&O Operational and Organizational 

OPLAN Operation Plan 

OPORD Operations Order 

ORD Operational Requirement Document

PA Public Affairs 

PDA Personal Digital Assistant 

PDF Portable Document Format 

PIR Priority Intelligence Requirement 

PS Postscript 

PSYOP Psychological Operations 

RDF Resource Description Framework 

RSS Rich Site Summary 

SF Special Forces 

SME Subject Matter Expert 

SPOD Sea Ports of Debarkation 

SSC Small Scale Contingency 

STRAP System Training Plan 

TESS Tactical Engagement Simulation System

TM Technical Manual 

TP Training Plan 

A-2 
TRADOC U.S. Army Training and Doctrine Command 

TSP Training Support Package 

TTP Tactics, Techniques, and Procedures 

UA Unit of Action 

UAMBL Unit of Action Maneuver Battle Lab 

UE Unit of Employment 

UJTL Universal Joint Task List 

URL Uniform Resource Locator 

USAARMC U.S. Army Armor Center 

XML Extensible Markup Language 

A-3