Improving Army Aviation Situational Awareness with Agent

Improving Army Aviation Situational Awareness with
Agent-Based Data Discovery
Peter Gerken, Stephen Jameson, Brian Sidharta, and Joyce Barton
Lockheed Martin Advanced Technology Laboratories
Camden, NJ
[pgerken, sjameson, bsidhart, jbarton]@atl.lmco.com
ABSTRACT
The Army Airborne Command and Control System (A2C2S) is being developed to provide situational awareness to the Joint
Task Force commander and staff on-the-move in a UH-60 Blackhawk helicopter. The Airborne Manned/Unmanned Systems
Technology Demonstration Science and Technology Objective program and the Hunter Standoff Killer Team Advanced
Concept Technology Demonstration program are developing the Mobile Commander’s Associate (MCA) decision aiding
system to support the command team on board the A2C2S. MCA will improve the command team’s capabilities by providing
shared situation awareness, decision aiding, and unmanned air vehicle control and control handover among teamed platforms.
Intelligent agents embedded in the MCA decision aiding system offload some of the command staff’s manual-intensive tasks
by autonomously monitoring for information that improves the commander’s situational awareness. Lockheed Martin
Advanced Technology Laboratories, under contract to the Army’s Aviation Applied Technology Directorate, provides
Distributed Data Fusion and Agent-Based Data Discovery components that enable situation awareness for these programs.
Introduction
Future Army Aviation operations depend on the development of technologies and systems that support effective onthe-move command of airborne and ground-based maneuver
forces through shared situational awareness and decision
aiding technologies. The operational concepts for these
technologies and systems are characterized by the extensive
use of mobile sensing systems, unmanned platforms, and
decision aiding systems in the forward elements of the
combat force. As part of this effort, the A2C2S is being
developed to provide situational awareness to the Joint Task
Force commander and staff on-the-move in a UH-60
Blackhawk helicopter [1]. The A2C2S combines comprehensive battlefield communication capabilities with a
sophisticated computing facility hosting the Army Battle
Command System (ABCS) suite of command and control
software systems into a mobile command system from
which the mobile commander can maintain command and
situational awareness both in flight or on the ground. Two
A2C2S prototypes are currently deployed with units at Ft.
Hood and Ft. Campbell, and a production contract is in place
to equip additional aircraft with A2C2S.
The Airborne Manned/Unmanned Systems TechnologyDemonstration (AMUST-D) Science and Technology
Objective and the Hunter Standoff Killer Team (HSKT)
Advanced Concept Technology Demonstration Programs,
____________
Presented at the American Helicopter Society 59th Annual Forum,
Phoenix, Arizona, May 6-8, 2003. Copyright © 2003 by the
American Helicopter Society International, Inc. All rights reserved.
led by the U.S. Army Aviation Applied Technology
Directorate (AATD) at Ft. Eustis, VA, are developing additional capabilities to support the mobile commander. The
Mobile Commander’s Associate (MCA) decision aiding
system being developed under AMUST-D provides capabilities to: improve the commander’s situation awareness,
support command of a heterogeneous airborne team, and
provide organic control and exploitation of an unmanned air
vehicle (UAV).
The MCA development effort leverages technology from
several previous advanced technology efforts. It incorporates
decision aiding, route planning, and data fusion technology
developed under the Rotorcraft Pilot’s Associate (RPA)
program. Team management technology developed under
the Tactical Execution Decision Aid (TEDA) ACT II
program and UAV control technology developed under the
preceding AMUST-Baseline (AMUST-B) program also
contribute to MCA.
One primary focus of this technology is to provide improved
commander situational awareness. Multi-Sensor Data
Fusion, Decision Aiding, and organic control of a UAV all
contribute to improving commander situational awareness
by improving access to—and utility of data from—a variety
of sensor sources. At the command level, however, much of
the information that contributes to situational awareness
does not come from specific sensors reporting on the
battlefield. Instead, much necessary information is contained
in a diverse set of heterogeneous distributed data sources
such as the Joint Common Data Base (JCDB) that serves as
the data repository for the ABCS applications. Making
effective use of this information requires tailored retrieval
strategies, persistent intelligent monitoring, and complex
interoperability protocols.
This paper describes the Agent-Based Data Discovery
technology and its application in AMUST-D and HSKT. We
also describe work that has been done to gather data on the
effectiveness and benefits of this technology in promoting
situational awareness among a distributed team of platforms.
We present the results of this work and offer conclusions
about the potential operational benefits of these technologies.
Background
Army Airborne Command and Control System (A2C2S)
The U.S. Army has long recognized the need to have mobile
command and control platforms to accommodate the needs
of a maneuver commander to provide command on-themove. Such situations can arise during a rapidly evolving
high intensity conflict, in a situation like Afghanistan where
forces are spread over a large geographic area with no fixed
borders between areas of friendly and hostile control, or
during humanitarian assistance operations in which on-site
presence of the commander is required. Previous systems did
not permit access to the increasingly digitized Army and
Joint Command and Control infrastructure. To address this
shortfall, the U.S. Army Aviation Applied Technology
Directorate (AATD) began development of a prototype
system in support of PEO C3T and PEO Aviation,
designated the Army Airborne Command and Control
System (A2C2S), that incorporated comprehensive digital
and voice communication capabilities as well as the
computing resources required to host a subset of the Army
Battle Command System (ABCS).
The A2C2S, housed in the rear of a UH-60 Blackhawk
helicopter, is configured as a 5-seat mobile command post
(Figure 1). Each seat has a flat-panel display and keyboard,
which can be independently switched to access one of eight
computer processors housed in a rack. These processors run
a set of ABCS applications including the Maneuver Control
System (MCS), All-Source Analysis System (ASAS), and
the Army Field Artillery Tactical Data System (AFATDS).
These applications provide the commander with a common
picture of the battle through the integration of near-real-time
situational information and sensor data into a force level
Courtesy of AATD
Intelligent Agents, an emerging technology being increasingly applied to military operational problems, are
ideally suited to addressing these requirements. ATL has
been developing intelligent agent technology and applications for DARPA and DoD laboratories for the past eight
years. Under the AMUST-D program, ATL has leveraged
this extensive experience to develop the Agent-Based Data
Discovery component that provides retrieval, monitoring,
and interoperability capabilities to the MCA.
Figure 1. Artist’s conception of A2C2S in a UH-60
Blackhawk accommodating the commander and four of his
staff.
database. Two larger displays at the front of the compartment, also independently switchable, allow important data to
be displayed to the entire staff. The A2C2S also includes
communication capabilities to permit voice and data access
by the commander and his staff to all required channels and
tactical data links, including access to the Tactical Internet
and voice communication by SINCGARS and SATCOM
radios.
Two A2C2S prototype systems have been developed to date.
Each prototype includes the consoles and displays, rackmounted computing systems, and the required radio systems,
installed in a specially modified UH-60L aircraft. The first
demonstrator system was deployed with the 4th Infantry
Division at Fort Hood, TX, in June 2001, and the second
soon thereafter with the 101st Air Assault Division at Fort
Campbell, KY. A production contract for an initial buy of
eight systems has been let to Raytheon, and the first delivery
of production units is scheduled for later this year.
Mobile Commander’s Associate (MCA)
The MCA is a decision aiding system [2] developed under
AMUST-D. It consists of a set of software modules in an
open-architecture framework built on top of an Open Source
software infrastructure and COTS hardware [3]. Major
components of MCA include Decision Aiding, UAV
Control, GUI, Data Fusion, and the Agent-Based Data
Discovery capability described in this paper.
The MCA Decision Aiding components support a maneuver
commander in managing a mission involving a mixed team
of joint airborne units, including attack helicopters, recon
helicopters, Air Force or Navy strike aircraft, and UAVs.
Mission management capabilities include creating and
editing a mission plan, automated planning and dissemination of routes for team members, managing team
composition, and monitoring team routes for threat and other
violations.
MCA UAV Control includes capabilities to permit a
maneuver commander to exploit the capabilities of an
organic surveillance UAV in developing situational awareness and monitoring the battle. UAV Control capabilities
include managing two-way communication with a UAV via
Tactical Common Data Link (TCDL), dissemination of
routes to the UAV, managing the UAV sensor, keeping track
of area scanned by the sensor, and monitoring UAV flight
for threat or other violations.
itinerary contains a set of activities from a set of building
blocks called tasks. A collection of generic tasks can be
written that can be reused in multiple activities. An agent
system consists of one or more machines that contain a dock
that manages when an agent is launched. When the dock
launches an agent, it determines where its activity needs to
run and, if necessary, the agent migrates to another dock
(Figure 2).
The MCA GUI includes sophisticated visualization features
to enhance the commander’s situational awareness. Using a
common standard look-and-feel, based on the ABCS lookand-feel, the GUI provides capabilities to display battlespace
entities, routes, plan features, and other control measures as
overlays on a variety of map, chart, and imagery formats.
MCA Data Fusion is based on the RPA Data Fusion
described in the next section, augmented with the Grapevine
capability [4] to provide a Distributed Data Fusion [5]
capability linking the MCA with the Warfighter’s Associate
being developed under AMUST-D for deployment on the
Apache Longbow helicopter.
Rotorcraft Pilot’s Associate (RPA)
From 1993 to 1999, ATL participated in the Army’s Rotorcraft Pilot’s Associate (RPA) Advanced Technology
Demonstration program, sponsored by AATD. ATL
developed the multi-sensor Data Fusion (DF) system that
provides a common fused track picture to the RPA pilot and
RPA decision aiding systems. The DF system fused kinematic and classification data of up to 200 battlefield entities
from 14 different types of onboard and offboard sensors in
real time. The RPA system was successfully flight demonstrated on an AH-64D in August 1999.
Intelligent Agent Technology
In the context of this paper, an intelligent agent is defined as
a persistent software process that is able to interact with its
environment in order to perform tasks on behalf of the user.
An intelligent agent has the following three key characteristics. An agent is autonomous because it is given a set of
instructions to perform without direct control of a human
operator. An agent is adaptive because it is able to adjust
what it will do based on its environment. An agent is
cooperative because it can work together with other agents
or with a human operator to accomplish its goals.
ATL has been developing agent technology since 1995 to
support various interoperability roles such as data dissemination, retrieval, and monitoring of key information
stored in legacy, stove-piped applications. At the core of this
agent technology is an agent architecture called Extendable
Mobile Agent Architecture (EMAA) [6]. EMAA is a JavaTM
based framework that allows a developer to create software
agents by building an itinerary for the agent to follow. An
Figure 2. Agents may migrate to another dock to access
local resources and then return with the information.
EMAA agents have been used in a variety of projects
beginning with the Domain Adaptive Information System
(DAIS). DAIS used agents to query heterogeneous databases
over a low bandwidth and unreliable military network. The
agents would migrate to where the data was stored, process
the data, and return the information analysts needed to
perform their jobs. DAIS was deployed with the Army 201st
Military Intelligence (MI) brigade at Fort Lewis and became
part of their standard operating procedures. During the
exercises, the agents were able to find and retrieve valuable
information within minutes rather than days. The information the agents gathered was key to accomplishing the
exercise’s objective [7].
EMAA agents can connect legacy stove-piped applications
together and allow them to interoperate with each other.
ATL has participated in six of the last seven Fleet Battle
Experiments with the Navy where agents allow the existing
applications to share information to solve problems such as:
theater air missile defense, time critical targeting, and track
management within a coalition environment.
Since EMAA agents generally have access to a large amount
of data, it is easy for them to also monitor for specific events
and alert the user when the events occur. This was first
demonstrated in a project for the U.S. Air Force Air Mobility Command where agents were tasked to monitor for
scheduling conflicts in an air mobility plan [8].
EMAA agents can improve the quality of the Common
Tactical Picture (CTP). Previously, DF would only accept
sensor data information. For the Army ACT II proof-ofconcept demonstration at the Army Air Maneuver Battle
Laboratory, DF used sentinel agents to find better quality
data in the Joint Common Database (JCBD) and fused the
new data with the current CTP [4,9].
ATL is leveraging the work done on these and other projects
in AMUST-D to allow the agents to find and monitor
information in a variety of heterogeneous data sources to
give the A2C2S commander improved situational awareness
of the battlespace.
Agent-Based Data Discovery (ABDD)
ABDD’s role in MCA is to use agents to retrieve relevant
information from the JCDB, provide interoperability among
a variety of applications, monitor for specific events and
alert the commander when those events occur.
Data Retrieval
Agents are tasked to find additional information stored in the
JCDB that is not available to Distributed Data Fusion (DDF)
through sensor inputs. All of the ABCS systems with in the
A2C2S have access to the JCDB and periodically update it
with information. Based on the JCDB’s schema, we have
discovered that tables exist to store information about blue
organizations, red organizations, facilities, and control measures. Agents retrieve this information and store it in an
internal cache for use by MCA. Agents supplement the
tactical picture by retrieving control measures from the
JCDB and forwarding them to MCA for display to the
commander.
Interoperability
One of the main strengths of agents is that they facilitate
interoperability within a collection of applications.
Knowledge acquisition efforts at the Central Technical
Support Facility at Fort Hood, TX and the Air Maneuver
Battle Laboratory at Fort Rucker, AL, have identified and
verified several applications and their data sources. In
AMUST-D, agents will share information among the
following components: the Live Feed Server (LFS) provided by ABCS; Distributed Data Fusion (DDF) developed
by ATL; and both the Decision Aid (DA) and Link 16
components developed by LM Owego (Figure 3). ABDD, in
turn, supplies data to DDF and other MCA components.
LFS
Blue Platforms,
Blue Units,
Red Spot Reports
DDF
Ta
ct
ica
lP
Se
ict
ns
ur
e
or
R
ep
or
ts
ABCS
Tactical
Picture, Alerts
ABDD
JCDB
Control Measures,
Blue and Red Units
DA
User-Entered Tracks,
Control Measures
Tr
M ack
an N
ag um
em b
en er
t
Because the tactical picture quality from DDF is limited by
the quality of the input sensor reports, agents attempt to
improve the tactical picture by connecting to the LFS. The
LFS provides friendly platform and unit locations usually
once every 10-20 seconds and human-entered spot-reports.
The platform-level data is converted into sensor reports and
output to DDF.
Agents supplement the tactical picture by retrieving control
measures from the JCDB—the common data repository for
all ABCS components—and forwarding them to MCA for
display to the commander. Additionally, by using control
measures stored in the JCDB and received from MCA,
agents provide control measure monitoring and alerting
services to the commander.
The AMUST-D program has recently identified the need for
DDF to interoperate with Link-16. Link-16 is the tactical
data link used by the armed forces to share a common
operating picture. To become a participant on Link-16, there
is a set of track management responsibilities that must be
followed. Each participant has a list of reserved track
numbers. These track numbers are assigned to a track and a
numeric quality number is assigned. If another participant is
tracking the same track with a higher quality value, it
assumes reporting responsibilities using the original track
number. The book keeping of the track numbers can be a
tedious task and is well suited for agents to manage. DDF is
able to receive and fuse the tracks coming across Link-16,
and uses ABDD in an interoperability role to handle all of
the necessary track number management and reporting
responsibilities.
Monitoring and Alerting
Monitor agents are used to continuously check for the
occurrence of operationally significant events and send alert
messages in such cases. ABDD agents are used to provide
timely information on the location of friendly and hostile
entities to help avoid fratricide and to aid situational
awareness.
A2C2S
MCA
DDF, a multi-sensor data fusion system, provides the
platform-level tactical picture for MCA. ABDD receives the
tactical picture created by DDF, performs user-specified
filtering of entities, and converts the data into a format used
by other MCA components. Additionally, agents cache the
tactical picture to support monitoring capabilities.
Link 16
Figure 3. ABDD allows components within the A2C2S to
interoperate with MCA.
The agents monitor the current state of a set of control
measures. Control measures that can be specified include an
Engagement Area (EA), a Named Area of Interest (NAI), a
Targeted Area of Interest (TAI), a Phase Line (PL), or a
Helicopter Route.
While many applications exist that track enemy locations,
monitoring blue movements to avoid fratricide would also
be very useful. AMUST-D addresses this problem by using
agents to manage and monitor track information on blue
forces. When specific events occur, the agents warn the
commander of potential danger. There are several scenarios
in which agent monitoring can aid in fratricide avoidance.
An engagement area is where a commander intends to attack
and destroy the enemy. If a blue force unknowingly enters
an EA, it may be inadvertently hit by friendly fire. By
having an agent monitor each of the EA’s and send an alert
when a blue force is discovered to be in such potential
danger, the commander has the opportunity to call the blue
force away from the EA or call off an attack to avoid
fratricide (Figure 4).
Figure 6. Blue found near helicopter route.
Agents monitor the locations of red forces within control
measures. When a red force appears in a control measure
such as an EA or NAI, an agent sends an alert (Figure 7).
This alert could be considered a decision point that helps the
commander choose a course of action.
Figure 7. Red found in a named area of interest.
Figure 4. Blue found in an engagement area.
Phase lines (PL) are used to control and coordinate military
operations during a battle. Under normal circumstances the
blue unit notifies the commander when it is about to cross a
PL [10]. However, sometimes a unit inadvertently crosses
the line. To assure that the commander is aware of PL
crossings, an agent monitors the line and alerts the commander (Figure 5). It is up to the commander to determine if
the blue force crossed too soon or if the next phase of the
battle should continue.
Figure 5. Blue found crossing a phase line.
Agents also monitor helicopter routes to determine if the
helicopter will encounter blue forces along the route. If a
friendly entity is found to be near the route, an alert is sent
so that the commander can notify the affected blue forces
(Figure 6). This also helps the helicopter pilot determine
whether or not encountered entities are friendly or hostile,
preventing friendly fire.
The Commander’s Critical Information Requirements
(CCIR) help the commander make critical decisions and
determine or validate courses of action [10]. The commander
can configure the agents to find data that match the CCIR.
Monitoring can be for a specific affiliation (blue, red,
unknown, etc.) or a combination of affiliations. The commander can also assign custom monitoring behaviors for a
specific unit such as when the unit enters a particular control
measure.
Example Scenario
Before a mission begins, a plan is developed that lists the
objectives and courses of action needed to successfully
complete the mission. Various control measures are created
and entered into the Maneuver Control System and shared
with the rest of the ABCS systems by storing them in the
JCDB. These control measures include the following: a set
of engagement areas where the commander intends to attack
the enemy; a set of named areas of interests that the commander will need to monitor to determine which engagement
area to attack; and a set of phase lines used for control and
coordination of the mission. The commander briefs his staff
to look for specific information that will help him determine
his course of action during the mission.
The following scenario demonstrates how agents might be
used to improve the commander’s battlespace awareness and
prevent fratricide. The control measures and locations of
various alerts generated in the scenario are shown in Figure
8.
When the mission begins, the ground tactical operations
center updates the A2C2S aboard the Blackhawk. The commander’s staff configures the agents to monitor the control
For Build 1, the agents were able to connect and share data
among DDF, MCA and the JCDB. They were also able to
monitor control measures from MCA and generate alerts
when a set of tracks were found in an engagement area.
During the evaluation, three U.S. Army pilots were given the
opportunity to execute the MCA software and provided
valuable feedback. The pilot’s feedback included having a
configuration panel available to configure various agent
parameters such as: which control measures to monitor,
which affiliation to monitor for, and which specific entity
should be monitored. This will allow the commander to
indirectly track his CCIR.
Figure 8. An example scenario showing where an agent
would generate alerts.
measures that relate to the CCIR. The commander boards the
helicopter and proceeds to an observation point near the rest
of the troops. On the way, the agents are automatically
scheduled to monitor the pre-configured control measures.
Early in the mission, the first alert appears on the MCA
display reporting that a blue force has crossed over a phase
line. Normally, the blue force would contact the commander,
however, they did not, so the commander sends a message to
pull back from the phase line and regroup with the rest of the
troops.
As the mission continues, an agent finds a hostile track in
one of the named areas of interest and sends a second alert to
the MCA display. The commander sees the alert and determines into which engagement area the hostile tracks are
heading. He gives the order for the attack helicopters to
engage without realizing that some of the blue forces are
also approaching the engagement area. The MCA route
planner determines the best route and sends the route to the
attack helicopters.
As the helicopters approach the engagement area, a third
alert appears on the MCA display. Some of the blue forces
have inadvertently crossed into the engagement area. The
commander alerts the blue forces to check their position and
alter their course away from the engagement area. As the
blue forces move away, a fourth alert appears on the MCA
display showing that their new course places them near the
attack helicopter’s route. The commander sends a warning
message to the attack helicopters that blue forces will be
encountered along the route. The helicopters approach the
engagement area just as the blue forces are safely away and
attack the enemy targets.
Preliminary Results
MCA will be going through a series of evaluations. The first
evaluation was held at Lockheed Martin Owego during
December 2002. The second evaluation will be held at Fort
Rucker in May 2003. Formal flight test will occur during the
fall of 2003.
For future releases of the agent technology under AMUSTD, ATL is planning to build agents to monitor phase lines
and helicopter routes, and allow the agents to be configured
by a configuration panel and handle the book keeping
needed to interoperate with Link 16.
Agents in an Embedded Environment
Many of our agent applications have been developed and
demonstrated on laptops and desktops that generally have
fast processors and large amounts of memory. On A2C2S,
the agents will be running using the Blackdown implementation of JavaTM on a 375 MHz PowerPC single board
computer running a real time Linux operating system with
128 MB of memory. ABDD shares these resources with
DDF, and employs several control paradigms to achieve an
efficient yet flexible system.
Agent Management
We designed each agent to be retaskable, which allows an
agent to be reused repeatedly for different jobs. Each agent
exposes an operational template where functional components called tasks can be plugged into the agent. The agent
can be reused for a different job simply by replacing its task
list. This also provides flexibility and extensibility in the
system, since the addition of new functionality requires only
the creation of new tasks.
The system also uses agent pools that provide a constrained
resource usage capability to the system. Agent pools cache
idle agents until they are needed again. If the agent pools
detect that the system is using too much memory or
processor time, they will reduce the number of agents in the
application. Also, agents are reused instead of being created
and deleted, which decreases the cost of repeatedly allocating and deallocating memory. Finally, on system startup, the
agent pool creates and stores a small number of agents. This
shifts the agent-creation cost to system startup and allows
agents to be dispatched more quickly.
A scheduler provides the centralized agent-dispatch manager
for the system that controls when agents are dispatched and
assures quality-of-service for requests. As processing jobs
are created, they are submitted to the scheduler, which deter-
mines when the job should be assigned to an agent. For jobs
that the user initiates, the scheduler immediately assigns the
job to an agent. If the scheduler determines that resources
are low, it prioritizes the jobs so time-critical jobs are started
as quickly as possible and delays certain jobs to avoid spikes
in resource usage.
Clustering
As conventional situational assessment applications must
typically be able to handle thousands of tracks in the battlespace, we employ geographical clustering to optimize track
searches. The clustering algorithm groups together tracks
that are positioned within close proximity to each other. This
allows the system to use fast bounding-box checks to quickly determine which tracks are near a search area and reduces
the problem to a manageable size.
Conclusion
The number of applications and amount of data in the
battlespace will continue to grow as the armed forces
become a digitized force. We have described how ATL’s
agent technology can currently be used to manage and
monitor the information being generated. We have shown
how agents are able to improve the situational awareness of
the commander by monitoring and alerting on the information they gather from DDF, MCA and ABCS. This additional awareness gives the commander an advantage on the
battlespace and allows him to autonomously monitor the red
forces and avoid friendly fire with the troops under his
command. This paper also described how we are managing
our agent technology in an embedded environment with
limited memory and processor constraints. The agent work
done under the AMUST-D project continues to build a
foundation of the shared situational awareness capabilities
that will be needed in the future to support the mobile
commander.
Acknowledgments
This research was partially funded by the Aviation Applied
Technology Directorate under Agreement No. DAAH10-012-0008. The U.S. Government is authorized to reproduce
and distribute reprints for Government purposes notwithstanding any copyright notation thereon.
representing the official policies, either expressed or
implied, of the Aviation Applied Technology Directorate or
the U.S. Government.
Previously published is a detailed discussion of DDF which
also mentions the use of agents [2], however, the paper for
this year’s conference focuses on the data discovery, monitoring and alerting capabilities of the agents.
References
[1] Gannon, S., “Integration of US Army Command and
Control Systems into a UH-60 Blackhawk,” American Helicopter Society Forum 58th, Avionics and Systems Session,
Montreal, Canada, June 2002.
[2] Stiles, P., Bodenhorn, C., and Galamback, K., "Mobile
Commander's Associate for AMUST-D," American Helicopter Society 59th Annual Forum, Phoenix AZ, May 2003.
[3] Hrustich, J., "Commercial and Open Source Software in
Rotorcraft Avionics Systems," American Helicopter Society
58th Annual Forum, Montreal, CA, June 2002.
[4] Pawlowski, A. and Stoneking, C., “Army Aviation
Fusion of Sensor-Pushed and Agent-Pulled Information,”
American Helicopter Society 57th Annual Forum, Washington, DC, May 2001.
[5] Jameson, S.M. and Stoneking, C., "Army Aviation
Situational Awareness Through Intelligent Agent-Based
Discovery, Propagation, and Fusion of Information,"
American Helicopter Society Forum 58th, Avionics and
Systems Session, Montreal, Canada, June 2002.
[6] Lentini, R., Rao, G., and Thies, J., “EMAA: An Extendable Mobile Agent Architecture,” AAAI Workshop, Software Tools for Developing Agents, July 1998.
[7] Whitebread, K. and Stein, H., “Domain-Adaptive Information Systems (DAIS),” Proceedings of the Second International Conference on Autonomous Agents (Agents ’98).
[8] McGrath, S., Chacón, D., and Whitebread, K., “Intelligent Mobile Agents in the Military Domain,” Fourth International Conference on Autonomous Agents, June 2000.
We would like to thank AATD for their continued support of
our agent technology. We would also like to thank the many
people at Fort Hood, TX and Fort Rucker, AL for allowing
us to use their facilities and supporting our efforts to test
connectivity to the ABCS systems and the LFS.
[9] Hess, J. and Patteson, S., "Adapting and Expanding
Rotorcraft Decision Aids for the Battle Commander," American Helicopter Society National Forum 57, Washington,
DC, May 2001.
Disclaimers
[10] Wade, N. (Ed), The Battle Staff SMARTbook, USA:
The Lightning Press, 1999.
The views and conclusions contained in this document are
those of the authors and should not be interpreted as