Collaboration in Network-Centric Warfare – Modeling Joint Fire Support Teams

Christian Gerstner, Robert Siegfried

Universitat der Bundeswehr MunchenWerner-Heisenberg-Weg 3985577 Neubiberg, Germany

{christian.gerstner|robert.siegfried}@unibw.de

Nane Kratzke

University of Applied Sciences LubeckMonkhofer Weg 239

23562 Lubeck, Germanykratzke@fh-luebeck.de

Abstract—This paper presents an agent-based model to com-pare different coordination patterns in joint fire support (JFS)scenarios. Modern warfighting approaches depend heavily on aseparation of concerns (like reconnaissance, coordination andengagement) and therefore impose high requirements on thecoordination of all involved parties. Following the GeneralReference Model for Agent-Based Modeling and Simulation(GRAMS), we present an agent-based model of this problemdomain. Our simulations indicate that decentralized JFS coor-dination leads to smaller average times from identification ofa target to final engagement, while at the same time requiringextensive resources. Central coordination is more effective interms of engaged units and reduced resource requirements, buttends to take more time.

Keywords-Modeling and simulation; Multi-agent simulation;GRAMS reference model; Network-centric warfare; joint fire.

I. INTRODUCTION

Joint Fire Support (JFS) is a military term for providing

lethal engagements in an ad-hoc manner in highly dynamic

warfighting scenarios. JFS requests are typically launched in

tactical situations by military ground units confronted with

non-predictable threats which can not be engaged by organic

engagement means of these ground units. JFS is realized by

military engagement, recce and on scene coordination means

provided by army, air force and navy units. These functional

nodes are assigned and combined ad-hoc.

A typical JFS request shall be executed within few min-

utes including the following tasks: determine adequate recce

and engagement assets, check rules of engagement, task

and reposition assets, collect and provide adequate target

data, conduct and assess the (lethal) engagement. A lot

of military command nodes on different command levels

may be involved in processing JFS requests properly and in

accordance with given rules of engagement. As JFS requests

can not be exactly forecasted in time, target location or class

nearly everything has to be coordinated ad-hoc.

A variety of national coordination patterns has evolved in

western countries including israeli armed forces to handle

this JFS problem domain. A coordination pattern is the com-

mand and control communication structure of command-,

engagement- and recce-nodes in order to collectively provide

a JFS service. None of the existing coordination patterns

seems to be adequate in every situation. Each one has advan-

tages as well as disadvantages. An optimal JFS coordination

pattern has to consider the extent and landscape of the

operational area of own forces, the amount of expected JFS

requests, defined areas of responsibilities of command nodes,

the amount of engagement, recce and on scene coordination

means capable to process JFS tasks as well as the applicable

chain of command.

The coordination patterns reach from strictly hierarchical

to completely decentralized (in vision) as well as hybrid

coordination patterns. Especially decentralized patterns are

reflecting modern warfighting approaches like network cen-

tric warfare visions [1], power to the edge approaches [2], in-

formation age combat models [3], [4] and resulting emergent

behaviour models [5] which make agent-based simulation an

obvious analysis approach.

This paper presents the inital version of an agent-based

model for analysing and comparing JFS scenarios as well as

JFS coordination patterns. We present our JFS model (which

is inspired by the information age combat model proposed

by Cares et al. [3], [4]) in section 2 and first simulation

results in section 3. Finally, we close with a conclusion and

an outlook on our ongoing research in section 4.

II. MODELING JOINT FIRE SUPPORT TEAMS

The development of the JFS model [6] is inspired by

the domain specific information age combat model [3],

[4] and follows closely the General Reference Model forAgent-Based Modeling and Simulation (GRAMS) [7], [8].

Therefore, the description of the model presented here is

structured according to the GRAMS reference model.

To get acquainted to the problem domain, the model

is restricted in many issues. Once the basics of the

problem-domain are well-understood, the restrictions may

be relaxed and a larger parameter space will be covered

(cp. [9]). Currently, three types of agents are distinguished:

the Reconnaissance-Agent, the Coordinator-Agent and the

Engagement-Agent. Each target is modelled as an object,

which means that it is not able to plan or react to his

environment like an agent. An action of a single agent is

triggered by an event, which on the other hand is triggered

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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DOI 10.1109/WI-IAT.2010.203

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Figure 1. Schematics of the model environment (T = Target, R =Reconnaissance-Agent, E = Engagement-Agent)

by the action of another agent, the environment or the agent

himself.

A. Aims of the model

The intention of the model is to evaluate different co-

ordination patterns. There are two possible directions: one

is to maximize centralization and the other is to minimize

it. Both coordination patterns have their own strengths and

weaknesses. The idea is to find the optimal pattern by

analyzing different parameters. These parameters can be the

time needed by a coordinator for finding and assigning a

required unit or the overall time needed until a specific target

is fought.

B. Macro-Level: Time and Environment

Time is modeled as discrete time steps. The duration of

each time step is not specified any further. This abstraction

seems feasible as the comparison of the coordination patterns

is purely qualitatively at the moment. Nevertheless, future

calibration and validation activities will adress this issue.

As indicated in figure 1, the environment is modeled as a

flat 2-dimensional matrix. Six different types of landscapes

are distinguished, namely forest, mountain, plain, city, sea

and inlandwater. The landscape determines the movement

possibilities of different unit types (army, air force, navy) as

well as specific limitations (e. g. reduced speed in mountain

areas).

C. Micro-Level: Objects and Agents

The Reconnaissance-Agents patrol along their routes,

which are defined by explicit waypoints (indicated by the

colored paths in figure 1). As soon as a target is located,

they stop and report this target to their superior Coordinator-

Agent. These commanders control all their subordinates and

evaluate their suitability of engaging this target. According

Visual perception

Acceptance of order[Marking necessary]

[Other unit ordered to marking]

[No marking necessary]

[has superior Coordinator][has no superior Coordinator]

Key:

Control-Flow

Data-FlowSensor

Send to Broadcastchannel Submit request

Move

Analyse effects of fire

Mark the target

Effector

Figure 2. Behavior of the Reconnaissance-Agent (depicted as sensor-effector-chains)

to a pre-defined prioritization method they choose one

subordinate Engagement-Agent and order him to fight the

target. If the kind of weapon fire makes marking necessary,

the reporting Reconnaissance-Agent or another available

Engagement-Agent is ordered to serve this marking at the

same timepoint as the weapon fire from the executing

combat unit. The result is controlled by the Reconnaissance-

Agent and reported to the commanding Coordinator-Agent.

The number of Coordinator-Agents as well as the actual

process of coordination is influenced heavily by the number

of coordinators and their hierarchy. For example, if there are

no Coordinator-Agents, the whole process is coordinated by

the Engagement-Agents themselves.

1) Target: At the current state, targets are represented by

immobile objects. Target-Objects appear according to a pre-

defined rate, and disappear according to some distribution

(thereby imitating moving objects which leave the specified

area of operations).

2) Reconnaissance: Reconnaissance units patrol on de-

fined waypoints and keep a given area under surveillance.

They have not the ability to fight, but to analyze and mark

a target if they detect one. If this event of detection happens

a recce initiates a request for fire support and sends it to the

command and control unit responsible for this part of the

environment (see figure 2).

Later on, after a unit is assigned to the target, the recce

is capable of marking the target, if the combat unit needs

marking (e. g. a fighter bomber using guided bombs). After

the unit has fired, the recce analyzes the impact on the target.

If the target is succesfully destroyed, the recce continues

with his patrol.

3) Command and Control: The command and control

units are represented by Coordinator-Agents which are not

located at any specific position within the given environment.

They control a specific rectangular area. All targets which

are detected within this area are reported to the responsible

Command and Control unit. The Command and Control

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Key:

Control-Flow

Data-Flow

Visual perception Acceptance of orderRead Broadcastchannel

Get status

[Suitable]

[Not Suitable]

[has no Superior]

[has Superior]

[Order = Analyse effects of fire] [Order = Mark]

[Order = Fight]

[Platform = Ship]

[Platform = Airplane]Check operating time

[Platform = Army/Infantry]

[Not enough Time]

[Enough Time]

Sensor

Submit request Move

Analyse appropriateness

Calculate prioritizationvalue

Send to Broadcastchannel

Move

Effector

Analyse effects of fire Fight the target Mark the target

Figure 3. Behavior of the Engagement-Agent (depicted as sensor-effector-chains)

units have an amount of Engagement-Agents subjected to

them. If a request for fire occurs, they evaluate the situation

and check all subordinates if the request can be fulfilled.

Each subjected Engagement-Agent is listed in a matrix

together with a value representing the suitability for the

reported target. This way the most appropriate Engagement-

Agent is identified and ordered to fight the target.

The Command and Control unit may come to the con-

clusion that no one of his subordinates can fight the target.

In this case, the Coordinator-Agent first tries to pass the

request along to his superior Command and Control unit (if

he has one) or to a neighbouring commander. If all this is

not possible or this agent is on top of the hierarchy, he puts

the request in a queue and checks the feasibility again later.

4) Engagement unit: Figure 3 illustrates the behavior of

the Engagement-Agent. The Engagement-Agent waits at his

starting point until he is ordered to fight a target. As soon

as he gets an order, he starts moving to the target until it

gets in the range of his weapons and attempts to destroy the

target. If the range of his weapons is higher then his line of

sight he has a need for a marker to help him marking the

target. This marker can be any other Engagement-Agent or

the reporting Reconnaissance-Agent if they are capable of

marking. After the target is fought, the agent controls his

operating time if he can be assigned to another mission or

if he has to move back to his starting point.

Together with this agent the problem of decentralization

shall be explained: If there are no superior Command

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# Coordination Short description

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# Coordination Short description1 centralized 3 coordinator, 3 recce, 9 engagement2 decentralized 3 recce, 9 engagement3 centralized 3 coordinator, 6 recce, 18 engagement

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# Coordination Short description1 centralized 3 coordinator, 3 recce, 9 engagement2 decentralized 3 recce, 9 engagement3 centralized 3 coordinator, 6 recce, 18 engagement4 decentralized 6 recce, 18 engagement5 centralized 5 coordination, 3 recce, 9 engagement

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# Coordination Short description1 centralized 3 coordinator, 3 recce, 9 engagement2 decentralized 3 recce, 9 engagement3 centralized 3 coordinator, 6 recce, 18 engagement4 decentralized 6 recce, 18 engagement5 centralized 5 coordination, 3 recce, 9 engagement

Figure 4. Average time from identification of a target to final engagementin different scenarios.

and Control units the Engagement-Agents have to manage

among themselves. Because there is no hierachy between

all the Engagement-Agents they have to communicate with

each other to identify the most appropriate agent. To do this,

the reporting agent publishes the request into a broadcast

channel which is accessable by all free unbound agents.

Each agent now evaluates the situation by calculating a value

representing his appropriateness. This value is published by

all agents into the public broadcast channel so that every

agent gets to know the value of all agents. Each receiving

agent now can check if his own value is the highest or if

there is any higher value published by another agent. This

way the most appropriate agent can be clearly identified and

orders himself to fulfill the task of fighting the target.

III. RESULTS AND EXPERIENCES

A. Results

After successful implementation of the model, nine dif-

ferent scenarios were simulated. In these scenarios we tested

three different ways to identify the most appropriate engage-

ment unit while using different approaches of prioritization.

One way of prioritization was to command the unit with the

shortest way to the target, which thus can engage the target

the fastest. A second approach is to identify the engagement

unit, which is just able to fight the target. This way higher-

ranked units are saved for more dangerous targets while

assigning combat units according to their strengths. The third

approach is a mixture of the first two and should combine

the advantages of both.

Each of the nine scenarios was simulated 100 times to

get average values. Figure 4 illustrates our main findings:

Although basically flat hierachies are aimed for, they reach

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their limit quite fast in the constraints of reality. First and

foremost, a huge amount of combat units is needed for

optimal coverage in decentralized coordination. Also, perfect

communication has to be ensured between all units to enable

the necessary interaction and coordination as well as to

avoid multiple fighting of the same target. In summary,

decentralized coordination leads to smaller average times

from identification of a target to final engagement, while

at the same time requiring extensive resources. Central

coordination is more effective in terms of engaged units and

reduced resource requirements, but tends to take more time.

B. Experiences

The most difficult part of the development was to ensure

the correct coordination between the agents. Modeling the

various information exchange relations and the subsequent

activities to be carried out by the agents is a challenging

task. Even though only three different types of agents were

considered, it is difficult to keep track of the intricate

interplay of mutiple agents.

By following the GRAMS reference model to develop

the agent-based model, we could focus purely on domain-

specific issues. In this sense, the GRAMS reference model

served very well as a guideline throughout the development

process. The strict seperation of events and actions defined

by the GRAMS reference model turned out to be helpful

also. This separation allowed us to construct complex event-

action chains where each event could trigger different actions

at the same time, whereas these action could produce events

as well.

While being beneficial, these event-action chains caused

trouble at the same time. In fact, it turned out that they could

hardly be analysed and debugged. This is not necessarily

a drawback of the GRAMS reference model, but has at

least two reasons: First, the tool chain available does not

support specific aspects of the GRAMS reference model very

well and debugging features are far from complete. Second,

and perhaps more notably, this complexity of modeling

coordination patterns may be immanent to these kind of

models.

IV. CONCLUSION AND OUTLOOK

We presented an agent-based model to analyze the influ-

ence of different coordination patterns on so-called joint fire

support teams. In this (restricted) model, three different types

of agents had to coordinate themselves (Reconnaissance-

agents, Coordinator-agents and Engagement-agents). The

coordination patterns investigated in a first stage ranged

from a wholly centralized coordination to completely de-

centralized coordination. A thorough model validation still

outstanding, the first simulation results indicate slight ad-

vantages of decentralized coordination about a centralized

coordination. At the same time, our experience regarding

the modeling of coordination is that the complexity increases

very fast and even smaller scenarios (in our case, with three

different types of agents and less than 50 agents in total) are

quickly hard to overlook.

The trade-offs between centralized and decentralized co-

ordination in combination with the resource needs and

utilization will be in the focus of future work. Furthermore,

a lot of restrictions were made to reduce the complexity of

the first model version. This model was very helpful to get

acquainted to the problem domain. With this background

knowledge the restrictions may be relaxed and a larger

parameter space will be covered (cp. [9]). A first extension

is to implement moving targets which will add a lot of

complexity to the coordination of the agents. Also, improved

route finding algorithms for the recce agents are of interest

(cp. [10]). Finally, we want to calibrate and validate the

model as well as the parameters to move on from qualitative

to quantitative investigations.

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