Abstract—The survivability of a naval surface combatant

depends largely on the effective management of combat resources.

In terms of platform-centric self-protection, situation assessment

strategies and engagement policies governing weapon usage

influence effective management. Situation assessment strategies

enable the surface combatant to adapt to changes in the

battlespace. In the case of network-centric operations, the task

force’s ability to adapt to changes in the battlespace relies on the

information superiority gained through shared awareness.

Although shared awareness enables surface combatants to apply

situation assessment strategies to self-synchronize to the situation,

engagement policies governing weapons usage typically remain

platform-centric and rely on centralized command structures to

provide overall coordination.

The research presented, herein, examines the implementation

of intelligent agents to create a partially centralized, distributed

command structure that uses Contract Nets to coordinate tactical

responses across the task force.

Index Terms—Contract Nets, Intelligent Agents, Network-

Centric Operations

I. INTRODUCTION

NFORMATION is critical to meaningful interactions

between individuals, communities, businesses, and

governments. In fact, information is vital to any endeavor

where people must collaborate to accomplish specific goals.

Information systems aim to facilitate this collaboration by

providing capabilities such as collection, storage, processing,

and dissemination.

Prevalent in business communities, information systems

support day-to-day operations while also sustaining strategic

planning and decision-making. In fact, an information system

can provide a significant competitive advantage whereby a

business can respond more quickly to changes in the

marketplace than its competitors. Military organizations have

recognized the strategic advantage afforded by information

systems and have been moving towards information-enabled,

network-centric operations for more than a decade [1].

This work was supported in part by the Defense Research & Development

Canada (DRDC).

1. R. J. Martelli is with Lockheed Martin Canada, Ottawa, ON K2K 2M8

Canada. Tel: +1-613-599-3270; fax: +1-613-599-3282; e-mail:

rick.martelli@lmco.com.

2. L. Esmahi is with the School of Computing & Information Systems,

Athabasca University, Athabasca, AB T9S 3A3 Canada. Tel: +1-780-473-

8564; fax: +1-780- 675-6186; e-mail: larbie@athabascau.ca.

Network-centric operations aim to achieve information

superiority and, thus, gain a strategic and tactical advantage.

These advantages translate into an increased agility that

influences the networked combatant’s response to changes in

the battlespace that might otherwise jeopardize mission

success.

Force protection, the defensive ability of a task force, is a

key element to mission success. Current network-centric

operations rely on shared awareness to enable individual

combatants to self-synchronize (respond to new information in

kind rather than through external direction) in a cooperative

response to new threats. However, poor quality or conflicting

information can hinder the self-synchronization process

resulting in a less than optimal response across the networked

force.

The project detailed in this paper examined an approach for

the coordination and collaboration of networked combatants

that does not rely on self-synchronization. This approach

features autonomous agents that, fitted with appropriate goal-

oriented behavior, can achieve optimal response coordination

through a negotiation mechanism based on Contract Nets.

II. LITERATURE REVIEW

A. Command and Control (C2)

The act of command is distinct from the act of command

execution thereby allowing for some degree of

decentralization. For example, a command center issues orders

to combatants within its command. These combatants execute

the orders thereby decentralizing command execution. Lee and

Ghosh [2] characterize the various C2 organizations noting

that the traditional form of centralized command with

centralized or decentralized control is inferior to a completely

decentralized solution. The attack vulnerability of a single

command center feeds this assumption of inferiority. However,

the authors note that having a central command provides an

effective vehicle for synchronizing combatants where the

command center acts as the information and decision gateway.

Dekker [3] poses key questions for determining the best

combination of centralized and decentralized decision-making

within C2. In particular, Dekker explains that although a

centralized headquarters typically provides adequate facilities

for collecting information and issuing globally optimal

solutions, time constraints and communications infrastructure

may hinder the ability to develop a globally optimal solution.

Coordinating Autonomous Agents for Force

Protection Using Contract Net

Richard J. Martelli 1, Larbi Esmahi

2

I

Fourth International Conference on Autonomic and Autonomous Systems

0-7695-3093-1/08 $25.00 © 2008 IEEEDOI 10.1109/ICAS.2008.32

219

In fact, Dekker favors decentralization noting that the

transmission of orders is typically more compact than the

transmission of information required to formulate the orders.

Cebrowski and Garstka [1] also maintain that “battle time”

is a critical factor in the decision-making and execution of

tactics and recognize that network-centric operations can

provide a significant advantage in terms of reacting to changes

in the battlespace. Cebrowski and Gartska use analogies from

the commercial sector as motivation for moving to network-

centric operations. In particular, Cebrowski and Gartska

comment:

Network-centric warfare, where battle time plays

a critical role, is analogous to the new economic

model, with potentially increasing returns on

investment. Very high and accelerating rates of

change have a profound impact on the outcome,

"locking-out" alternative enemy strategies and

"locking-in" success. (p. 5)

Cebrowski and Gartska believe networked combatants can

synchronize themselves to the mission based on shared

awareness. Where centralized command is a top-down method

of synchronization and control, Cebrowski and Gartska believe

that a bottom-up process, such as self-synchronization through

shared awareness, can be just as effective.

Lee and Ghosh [2] take the notion of synchronization a step

further. They believe that within the chaos of battle, events

occur asynchronously with respect to individual combat units.

Hence, rather than focusing on synchronization through

centralization, Lee and Ghosh examine algorithms that

promote cooperation between decentralized command and

control centers. Mission objectives and common rules of

engagement form the basis for cooperation.

In this manner combatants are free to take independent

actions based on beliefs derived from locally available

information and shared awareness. Decisions formed from

these beliefs must adhere to the established mission objectives

and rules of engagement. A combatant communicates its

beliefs and decisions only to those combatants within its

sphere of influence as a means of reducing the risk of

information saturation that might otherwise interfere with

decision-making. Lee and Ghosh use simulations to

demonstrate the superiority of this approach against the more

traditional model of centralized command.

The simulation implements scenarios featuring both evenly

matched and unevenly matched forces. In each scenario, one

force utilizes decentralized command and control while the

opposing force establishes a central command structure. The

opposing force must process all changes in the battlefield at

the command centre, which, due to inherent delays in

processing and communications, results in less responsive

maneuvering and targeting, and ultimately fewer kills.

Lee and Ghosh claim faster reaction times to dynamic

information using the decentralized command structure but do

not discuss the role of centralized command in providing an

optimal response. One might assume that by reducing

processing and communications delays the centralized force

could approach or exceed the level of effectiveness of

decentralized command. This observation suggests that

decentralized command could benefit by coordinating

resources for an optimal response. This optimization would

involve an allocation of assets to specific targets that best

increases mission success.

B. Market-Based Approaches for coordination

Centralized command organizations are most likely to

develop globally optimal solutions. However, time constraints

warrant a decentralized approach using some form of

coordination to achieving an optimal response. When

considering optimization as essential to allocating assets to

specific targets of opportunity, coordination becomes a

resource allocation problem. In view of coordination as a

resource allocation problem, techniques from other domains,

such as market-based approaches, may be applicable to

decentralized command.

Market-based approaches deal with the concept of the

global good in an environment where self-interested agents

apply strategies that would otherwise maximize their own

good. The following sections discuss various market-based

approaches and their influence on the global good.

1) Auctions

Auctions are a means for mutually beneficial exchanges

ensuring that the seller receives fair market value while

bidders obtain items at or below their valuation. Typical to

most forms of auctions, bidders continue to place ascending

bids until no other bids are tendered. The final bid establishes

the price paid by the winner or all subsequent buyers.

The seller can choose the form of auction to influence a

better price but it is incumbent on individual bidders to

establish a valuation of the auctioned item and implement a

bidding strategy in line with that valuation and compliant with

the form of auction. For example, in an English (Ascending)

auction each bidder must bid a higher amount than the last

accepted bid to remain in the competition. To reduce the risk

of having to bid an amount greater than the item’s perceived

valuation, bidders will implement a dominant strategy whereby

bids are intentionally lower than the bidder’s valuation of the

item and, using small increments, higher than the leading bid.

In certain situations, the demand for an item may drive bids

above the item’s true valuation.

Wellman, Walsh, Wurman, and MacKie-Mason [4] examine

the English auction for decentralized scheduling of

computational resources. Agents bid according to tasks to be

completed whereby an agent’s bidding strategy aims to

maximize the surplus value across all jobs - the difference

between the agent’s maximum valuation and the actual bid.

The auctioneer establishes a reserve price for a time slot

based on demand. The reserve price encourages agents to

reassess preferences for particular time slots and maximize

their surplus value. Wellman et al. notes that this approach is

more appropriate for agents competing for a single unit.

Combinatorial auctions specifically support bidding on a

preferred bundle of items [5] [6].

220

A common consideration when applying auctions is the role

of the auctioneer. The auctioneer acts as a mediator and

attempts to resolve conflicts while encouraging a globally

optimal solution. Using auctions as a coordination technique in

network-centric operations would require a similar form of

mediation and mandate a more centralized command

organization that would promote the global good.

2) Contract Nets

Contract nets were first proposed as a means for distributed

problem solving [7] [8]. Through negotiation, contracts are

formed between the contract initiator and selected contractors.

Initial contract announcements include terms and conditions

that contractors must satisfy in order to participate. The

initiator evaluates the submitted bids and selects one or more

contractors, as necessary. Contract-net have been proposed for

adaptive workflow control [9] and for network-centric

operations [10]. When applied to network-centric operations, a

central command structure was used where it was noted that

reaction time was a constraining factor.

3) Other Approaches

Other approaches not based on market economies also apply

to coordination. These range from negotiation or bargaining

strategies to cooperative approaches such as blackboard and

voting systems. Although negotiation and bargaining strategies

apply to cooperative settings, the participants are typically

constrained at two. Blackboard or voting systems [11] are not

so constrained but, similar to auction, require some form of

centralization or mediation.

4) Discussion

In general, market-based approaches use bidding strategies

to establish globally optimal solutions for resource allocation

while the non market-based approaches reviewed tend to focus

more on collective problem solving. Auctions require a

mediator and successive rounds to develop an optimal solution

whereas non market-based approaches require some form of

centralization. Hence, neither of these approaches lends itself

very well to decentralized command and presents levels of

communications that might prove unaffordable.

In our context of force protection, contract-net may best

minimize the exchange of information and provide roles (i.e.,

contractor and participant) and messages to support

decentralization and coordination. By allowing more than one

networked combatant to assume the role of contractor while

preventing more than one contractor for any given missile

threat. A dynamic, partially centralized organization can be

achieved while also supporting the distribution of command.

Similar approaches used to coordinate large groups of agents

found a reduction in the overall complexity of coordination

when using dynamic, partial centralization [12].

III. NETSCHEDULER, A SIMULATION TOOL FOR RESPONSE

COORDINATION

A. Simulating Response Coordination

The simulation uses low-fidelity models for both self-

directed anti-ship missile threats and for surface combatants.

The Java-based platform-centric, Dynamic Engagement

Scheduler (DEScheduler) developed at Defense Research &

Development Canada (DRDC) provides the necessary low-

fidelity models. In this paper we are presenting the resulting

extension to the DEScheduler application herein referred to as

the NetScheduler.

Extending the platform-centric nature of the simulation to

include network-centric coordination involved the introduction

of autonomous agents. The agents interact using the Contract

Net Interaction Protocol and utilize roles (i.e., Mission

Commander and Mission Support) to support a dynamic,

partially centralized command structure.

Using Gaia [13], [14] an agent-oriented methodology,

models of the environment, roles, and interactions facilitate the

development of role schemas (see Table I).

TABLE 1: ROLE SCHEMA FOR THE MISSION COMMANDER

Role Schema: MISSION COMMANDER (MC)

Description: This role involves announcing the command of a mission, and

soliciting support as required.

Protocols and Activities:

IdentifyNewMissions, ExecuteMission, ReviewMissionObjectives, Announce, CallForProposal, FormulatePlan, ReviewProposals, RejectProposal, AcceptProposal, Cancel

reads System Tracks

changes Mission

changes Call for Proposals

consumes Proposals

Permissions:

changes Contracts

Liveness: MISSION COMMANDER = (ASSESS || COMMAND)ω

ASSESS = (IdentifyNewMissions.Announce) COMMAND = (ExecuteMission. (ReviewProposals.(AcceptProposal || RejectProposal) || Cancel ) || ReviewMissionObjectives.CallForProposals.FormulatePlan)

Safety: All threats (system tracks) have a mission commander

assigned

Schemas depict the protocols and activities supporting the

role while also outlining the agent’s influence on the

environment (permissions). “Liveness” and “Safety” describe a

role‘s responsibility where liveness (the desirable path)

expresses the logical flow of protocols and activities, and

safety (avoiding the undesirable) denotes the conditions

needed to maintain operational integrity.

As a society of agents, rules establish how agents can

assume roles within the society’s organization. For the purpose

of force protection, these organizational rules must specifically

address the dynamic nature of the command structure. The

organizational rules are defined as follows where s refers to

sensor information (tracks), and i refers to the agent

(combatant):

1. ))(,())(,(, sSupportiPlayssCommanderiPlaysis ¬∧∀⋅∀

2. ))(,())((),( sCommanderiPlayssCommanderisiCanEngageif →¬∃∧

3. )()())(),,((,, iSuccessjSuccessiffsCommanderjiSupercedePlaysji >∀

221

The first rule simply states that for all tracks s, agent i

cannot play the roles of Mission Commander and Mission

Support concurrently. The second organizational rule indicates

that an agent can assume the role of Mission Commander if the

agent can engage the threat and no other agent is currently in

that role. The third rule allows an agent to supersede the

Mission Commander if that agent deems its success criteria

offer a greater chance of mission success. This is necessary

due to delays in communication that would result in more than

one agent satisfying the second rule. In this situation, agents in

the Mission Support role would defer to the Mission

Commander with the higher probability of success. Note, an

agent may also choose to assume its own mission and agenda

based on its own need for survival.

B. Implementation of the NetScheduler

The Agent Model (Figure 1) defines the class architecture of

the agent system. This model incorporates intelligent agents

that, by design, are able to react to the environment, pursue

goal-directed behavior, and socially interact [15]. The agents

consist of the Weapons Manager, Sensor Manager, and

Command Link.

Figure 1. NetScheduler Agent Model

The Weapons Manager Agent specifically supports the

Mission Commander and Support roles, while the Command

Link Agent implements the Contract Net protocol. The Sensor

Manager Agent maintains the track information consumed by

the Weapons Manager Agent.

The features supported by the NetScheduler include the

main editors required for managing the simulations data:

• Scenario Explorer - used to access editors and simulation

features

• Engagement Scenario Editor (figure 2) - combines an

attack scenario with a defense scenario and establishes the type

of policies and constraints used during simulation. f

• Attack Scenario Editor (figure 3) - defines missile threats

and associated information establishing the attack profile.

• Defense Scenario Editor – defines the surface platforms

and configures the available weapon systems.

Figure 2. The Engagement Scenario Editor

Figure 3. The Attack Scenario Editor

Figure 4. The Defense Scenario Editor

222

The NetScheduler offers also the models and tools for control

and visualization. These tools include:

• Damage Assessment – establishes a simple probability

model for disabling systems in the event of a missile hit

against own ship.

• Simulation Execution (figure 5)– provides control over

simulation execution.

• Visualization Displays (figure 6 and figure 7)– includes

Network-centric and Platform-centric views.

• Data Logging - records data and event information for

post analysis

• Scenario Database - a central repository of all

engagement scenarios, attack scenarios, and defense scenarios.

Figure 5. The Defense Scenario Editor

Figure 6. The Defense Scenario Editor

Figure 7. The Defense Scenario Editor

IV. SIMULATION RESULTS

To determine the viability of using contract nets to establish

response coordination for task force protection we have

processed some simulations scenarios that allow us to compare

results from coordination that relies on a platform-centric

response (i.e., common rules of engagement) with those

obtained as part of a network-centric (i.e., coordinated task

force) response. Given the limited space we have for this paper

we present here only the summary of the results in terms of the

Measures of Merit (MOM) acquired via Monte Carlo

simulation techniques. For this simulation we used air defense

scenarios featuring four separate missile threats appearing at

distances exceeding sensor range to distances that significantly

reduce TTG (e.g., 10 km). The MOM presented, summarize

the recorded experimentation results denoting the number and

duration of engagements, engagement types (i.e., hard-kill,

soft-kill), engagement outcomes (i.e., success, fail, or aborted),

results (i.e., hit, miss, soft-kill, hard-kill), and weapon

inventory usage.

The network-centric response was shown to provide higher

effectiveness scores than platform-centric responses, with the

noted exception of soft-kill effectiveness (Table 2). The low

score of soft-kill effectiveness is a direct result of the

simultaneous deployment of hard-kill tactics.

The destruction of a missile threat by a hard-kill weapon

results in the suspension of any soft-kill assessment. The

cancellation of the soft-kill assessment results in the soft-kill

engagement being flagged as a failure. Hence, the soft-kill

effectiveness value does not provide an indication of the

efficacy of soft-kill tactics but viewed in conjunction with the

chaff inventory can give some indication to the coordination

provided by the network-centric response. This coordination

produced fewer soft-kill engagements and ultimately expended

223

fewer munitions.

TABLE 2: MEASURES OF MERIT (MOM) SUMMARY

Initial Range Platform-Centric

Response

Network-Centric

(Coordinated)

Response

Kill Effectiveness 96.6% 98.9%

Battlespace Efficiency

Ratio (BER)

71.1% 63.1%

Engagement

Effectiveness

21.4% 39.3%

Hard-Kill Effectiveness 34.9% 46.6%

Soft-Kill Effectiveness 4.0% 2.9%

Although the network-centric response demonstrated better

effectiveness, the efficiency score provided by the BER was

below that of the platform-centric response. The BER values

favor the platform-centric response where BER is a measure of

the task force’s ability to make use of the window of

opportunity to engage a missile threat. The lower BER for

network-centric responses is an indication of the time spent on

deliberation and coordination of the task force’s resources.

While the missile threat is beyond weapon range, the

difference in BER values is negligible because deliberation

and coordinating communications takes place before the threat

has entered weapon ranges. When missile threats appear

within weapon range, the time spent on deliberation and

coordination occurs within the window of opportunity to

engage the threat thereby reducing the actual time spent

engaging the threat and, thus, reducing the BER.

Although the BER for the network-centric response is lower

than that of the platform-centric response, the time spent on

deliberation and communications does not show any adverse

effect on the networked combatant’s defensive ability. In fact,

based on the average number of engagements and remaining

inventory, the resource consumption is lower for the network-

centric response.

V. CONCLUSION

In this project we examined the use of Contract Nets for the

coordination and collaboration of networked combatants

against anti-ship missile threats. This examination involved the

development of a simulated environment wherein each surface

combatant uses intelligent agents to formulate plans and

negotiate a coordinated response. Simulation results

demonstrated improved survivability with increased

effectiveness in the management of combat resources.

The simulation involved a comparison of surface

combatants using platform-centric engagement policies versus

those using intelligent agents for network-centric response

coordination. Combatants operating in platform-centric mode

formed a completely decentralized C2 structure while the

combatants using intelligent agents created a dynamic,

partially centralized organization. The resulting comparison

focused on ship survivability, battlespace management, and

resource usage. In particular, it was necessary to determine if

using Contract Nets for response coordination compromised

the task force’s defensive ability.

The results of the comparison did not reveal any

compromise to force protection. In fact, the results revealed an

improvement in survivability in the form of kill effectiveness

while also demonstrating a reduction in resource consumption.

A reduction in resource consumption increases the task force’s

ability to react to new situations. In other words, as new threat

detections occur, the availability of resources enables the task

force to respond with a broader range of options (i.e., long-

range or mid-range weapons). This broader range of options

increase the task force’s agility.

The command structure also influences the task force’s

agility. In the approach examined in this project, the intelligent

agents implemented a dynamic, partially centralized command

structure through roles facilitated by the contract net

interaction protocol. Beaumont’s use of a central coordinator

with Contract Nets found good survivability results. However,

Beaumont noted that communications and TTG were critical

factors [16].

This project’s results did not reflect this observation. In fact,

results based on the dynamic, partially centralized organization

showed consistent scores across all TTG values (represented

as ranges). In the case of communications, a partial

centralization could compensate in the event of failure. This

compensation would manifest as multiple Mission Command

roles being assumed. In addition, the intelligent agent’s goal-

directed behavior maintains the surface combatant’s ability to

take independent action. This independence enables the

surface combatant to rely on local information to set priorities,

formulate plans, and, when necessary, to give all priority to

defending itself.

Although the proposed dynamic, partially centralized

command structure was an attempt to evolve organization and

doctrine, as suggested by Alberts [17], in line with advances in

self-protection, it was necessary to circumvent some of the

situation assessment algorithms developed specifically for self-

protection. Due to the platform-centric nature of these

algorithms (i.e., lethality and soft-kill kill-assessments), a

surface combatant could prematurely assign a non-threat status

to an anti-ship missile based on its own self-centric evaluation

and not consider the threat to other combatants. Hence, further

work is required to progress concepts used in self-protection

towards more inclusive task force policy.

Coordination of tactics is another area that warrants further

work. Investigating how the deliberative planner could decide

upon which tactics to initiate from proposed plans was not

within the scope of this project. However, based on the

feasibility of Contract Nets demonstrated by the results, there

would be merit in growing the deliberative planner used in the

NetScheduler tool to include tactical coordination.

224

REFERENCES

[1] A. Cebrowski and J. J. Gartska, (1998, January) “Network-Centric

Warfare: Its Origins and Future,” in Proceedings of the Naval

Institute, 124(1).

[2] T. Lee and S. Ghosh (1996, Winter). “Simulating Asynchronous,

Decentralized Military Command and Control,” IEEE Computational

Science and Engineering, 3(4), pp. 69-79

[3] A. H. Dekker (2003, July), “Centralisation and Decentralisation in

Network Centric Warfare,” Journal of Battlefield Technology, 6(2), pp.

23-28.

[4] M. Wellman, W. Walsh, P. Wurman, and J. Mackie-Mason, Auction

Protocols for Decentralized Scheduling, Technical Report, University

of Michigan, July 1998.

[5] W. Conen and T. Sandholm, “Differential-revelation VCG Mechanisms

for Combinatorial Auctions,” Proceedings of the 4th ACM Conference

on Electronic Commerce (San Diego, CA, USA, June 09-12, 2003).

EC’03 ACM Press, New York, NY, pp. 196-197.

[6] E. Kutanoglu and S. D. Wu, (1999) “On Combinatorial Auction and

LaGrangean Relaxation for Distributed Resource Scheduling,” IIE

Transactions, 31, pp. 813-826.

[7] R. G. Smith, (1980) “The Contract Net Protocol: High Level

Communication and Control in a Distributed Problem Solver,” IEEE

Trans. On Computers, 29(12), pp. 1104-113.

[8] R. G. Smith, and R. Davis (January, 1981) “Frameworks for

Cooperation in Distributed Problem Solving,” IEEE Trans. On Systems,

Man, and Cybernetics, 11(1).

[9] T. H. Harker, and L. H. Ungar, (1996) “A Market-Based Approach to

Workflow Automation,” presented at NSF Workshop in Information

Systems Athens, GA, May 8-10.

[10] P. Beaumont, (2004) “Multi-Platform Coordination and Resource

management in Command and Control,” Master Thesis, Laval

University

[11] J. R. Parker (November, 1995) “Voting Methods for Multiple

Autonomous Agents, presented at ANZIIS ’95, Perth, Australia.

[12] P. Scerri, R. Vincent, and R. Mailler (July, 2004) “Comparing Three

Approaches to Large Scale Coordination,” Proceedings of the First

Workshop on the Challenges in the Coordination of Large Scale Multi-

agent Systems.

[13] M. J. Wooldridge, N. R. Jennings, and K. Kinny (2000) “The Gaia

Methodology for Agent-Oriented Analysis and Design,” Autonomous

Agents and Multi-Agent Systems, 3(3) pp. 285-312

[14] F. Zambonelli, N. R. Jennings, M. J. Wooldridge (2003) “Developing

Multi-Agent Systems: The Gaia Methodology,” ACM Transactions on

Software Engineering and Methodology, 12(3), pp. 317-369

[15] M. J. Wooldridge, An Introduction to Multi Agent Systems. West

Sussex, England: Wiley & Sons, 2002

[16] P. Beaumont (2004). Multi-Platform Coordination and Resource

Management in Command and Control, Master Thesis, Laval

University. Retrieved on November 29, 2006 from

http://damas.ift.ulaval.ca/projets/TeamWork/publications.en.html.

[17] D. Alberts, J.J. Gartstka, and F.P. Stein (2000). Network-Centric

Warfare: Developing and Leveraging Information Superiority 2nd

Edition (Revised), CCRP Publications. Retrieved on March 1, 2007

from http://www.dodccrp.org/html3/pubs_download.html.

225