Agent-Based Modeling of a Network-Centric Battle Team 

Operating Within an Information Operations Environment 

by Brian G. Ruth and J. Dana Eckart 

ARL-TR-2913 February 2003 

Approved for public release; distribution is unlimited. 
NOTICES 

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use thereof. 

Destroy this report when it is no longer needed. Do not return it to the originator. 
Army Research Laboratory 

Aberdeen Proving Ground, MD 21005-5068 

ARL-TR-2913 February 2003 

Agent-Based Modeling of a Network-Centric Battle Team 

Operating Within an Information Operations Environment 

Brian G. Ruth 

Survivability/Lethality Analysis Directorate, ARL 

J. Dana Eckart 

Virginia Bioinformatics Institute 

Approved for public release; distribution is unlimited. 
i

Acknowledgments 

The authors would like to thank Mr. Richard Sandmeyer of the U.S. Army Research Laboratory 

for his ongoing technical consultation during the course of the research presented in this report 

and for his careful and insightful technical review of the report. 
ii

Contents 

Acknowledgments i

List of Figures iv 

List of Tables viii

Executive Summary ix

1. Introduction 1

1.1 Purpose ............................................................................................................................1 

1.2 Background .....................................................................................................................1 

1.2.1 Network-Centric Complex Adaptive Systems ....................................................1 

1.2.2 Agent-Based Models ...........................................................................................3 

1.2.3 Cellular Automata ...............................................................................................5 

1.2.4 Genetic Algorithms .............................................................................................5 

1.2.5 Threat...................................................................................................................7 

1.3 Scope ...............................................................................................................................7 

2. The Cellular Automata-Based Combat Agent Model 8

2.1 The Cellular Simulation System......................................................................................8 

2.2 Building the Combat Agent Battle Team........................................................................8 

2.2.1 The Generic Combat Agent.................................................................................8 

2.2.2 Combat Agent Battle Team Representation......................................................10 

2.3 Combat Agent Dynamics ..............................................................................................11 

2.3.1 Firepower...........................................................................................................11 

2.3.2 Passive Self-Protection......................................................................................12 

2.3.3 On-Board Sensor ...............................................................................................12 

2.3.4 Communications................................................................................................13 

2.3.5 On-Board RF Jammer .......................................................................................13 

2.3.6 Supplies of UGS Units/Mines/Jammer Bombs .................................................14 

2.3.7 Signature............................................................................................................17 

2.3.8 Human-Based Attributes ...................................................................................17 

2.3.9 Mobility.............................................................................................................18 

2.3.10 Behavior ............................................................................................................19 
iii

3. Combat Agent Simulations 23

3.1 Blue/Red Unit Combat-Driven Coevolution.................................................................24 

3.1.1 Genomic Coevolution Framework ....................................................................24 

3.1.2 The combat_ga CA Simulation Engine.........................................................25 

3.1.3 Genomic Operational Fitness ............................................................................30 

3.1.4 Genetic Operations ............................................................................................35 

3.1.5 Genome Cost Filtering ......................................................................................36 

3.1.6 Information Operations Simulations .................................................................38 

3.2 Enhanced Network-Centric Quasiscripted Simulation..................................................48 

3.2.1 Simulation Framework ......................................................................................48 

3.2.2 The combat_proto CA Simulation Engine..................................................49 

3.2.3 “Fombler’s Ford”-Inspired Scenario.................................................................52 

3.2.4 Simulation Results.............................................................................................64 

4. Conclusions 82

5. References 84

Appendix. Blue/Red Genome Classification for the Coevolutionary Simulation 87

Report Documentation Page 111 
iv

List of Figures 

Figure 1. Triad of distributed battlefield entity types within a network-centric “system-ofsystems.” ....................................................................................................................................2 

Figure 2. Representation of a generic agent....................................................................................3 

Figure 3. Representation of a generic agent-based model. .............................................................4 

Figure 4. CA cell neighborhoods in two dimensions: (a) von Neumann neighborhood and 

(b) Moore neighborhood............................................................................................................5 

Figure 5. Iterative evolutionary processes within a genetic algorithm (from Ilachinski 1996)......6 

Figure 6. A generic combat agent.................................................................................................10 

Figure 7. Representation framework for a combat agent battle team configuration. ...................10 

Figure 8. The genetic schema representing hardware and behavioral attributes of a generic 

combat agent. ...........................................................................................................................11 

Figure 9. Operational dynamics of a UGS unit: (a) UGS unit senses its local environment 

and (b) UGS unit transmits an SA report on the sensed agent across a von Neumann CA 

neighborhood. ..........................................................................................................................16 

Figure 10. The extended Moore CA neighborhood surrounding a notional RF jammer bomb....17 

Figure 11. Software framework for simulating Blue/Red combat unit genomic coevolution......25 

Figure 12. Cyclic dynamics of the combat_ga CA simulation engine. ....................................26 

Figure 13. Demonstration of the intelligent relay network as implemented within 

combat_ga............................................................................................................................28 

Figure 14. Evaluation of collective combat unit time-averaged survival fitness 〈Fsurv〉.............31 

Figure 15. Genetic operations on two candidate combat unit genomes. ......................................36 

Figure 16. GA cost function for hardware-specific genes within a chromosome. .......................37 

Figure 17. Basic Blue/Red combat scenario set on a flat plane....................................................38 

Figure 18. Territorial importance gradient associated with the basic Blue/Red combat 

scenario. ...................................................................................................................................39 

Figure 19. Coevolutionary dynamics of Blue/Red time-averaged survival fitness levels (yaxis) measured across 1360 coevolutionary generations (x-axis)............................................43 

Figure 20. Coevolutionary dynamics of optimal Blue and Red unit configurations relative to 

the set of combat agent sensor/firepower range classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit........................................44 

Figure 21. Coevolutionary dynamics of optimal Blue and Red unit configurations relative to 

a set of combat agent behavioral classes with class population (y-axis) vs. coevolutionary 

generation (x-axis): (a) Blue unit and (b) Red unit.................................................................46 

Figure 22. Software framework for running the enhanced Blue/Red quasiscripted combat 

simulation.................................................................................................................................49 
v

Figure 23. Cyclic dynamics of the combat_proto CA simulation engine. .............................50 

Figure 24. Reduced RF jammer bomb neighborhood consisting of the center bomb cell plus 

eight neighboring cells.............................................................................................................51 

Figure 25. Notional Fombler’s Ford terrain imposed onto the CA lattice....................................53 

Figure 26. Territorial importance gradient associated with the Fombler’s Ford-inspired 

combat scenario. ......................................................................................................................53 

Figure 27. Blue C2V agent chromosome......................................................................................54 

Figure 28. Blue short-range direct-fire (DF) agent chromosome. ................................................54 

Figure 29. Blue long-range sensor agent chromosome.................................................................55 

Figure 30. Blue “rockets-in-a-box” agent chromosome. ..............................................................55 

Figure 31. Red “pickup truck” agent chromosome.......................................................................56 

Figure 32. Red medium-range DF agent chromosome.................................................................56 

Figure 33. Synchronization matrix for the Fombler’s Ford-inspired combat scenario.................59 

Figure 34. Four successive snapshots illustrating the deployment of a notional UGS network 

across neutral territory within the Fombler’s Ford landscape. ................................................60 

Figure 35. Four phases within a combat_proto simulation cycle using the Fombler’s 

Ford-inspired scenario: (a) Red agents detected within the peripheral UGS network, (b) 

sensor data propagates from sensed entities to sensor-equipped Blue agents, (c) acquired 

sensor data are shared over the Blue communication network, and (d) Blue fires on Red 

target. .......................................................................................................................................61 

Figure 36. Four successive snapshots illustrating the propagation dynamics of SA data 

throughout the UGS network...................................................................................................62

Figure 37. Preconfigured land mine field surrounding Blue territory. .........................................63 

Figure 38. The status of Red’s sensor web rip operation by the end of the 12th time cycle 

within a combat_proto simulation instance.......................................................................64 

Figure 39. Fombler’s Ford simulation results, with UGS density = 0.55, Blue fire point = 10 

cells, and 1 Blue shot/cycle: (a) combat unit survival and (b) Blue sensor coverage.............66 

Figure 40. Fombler’s Ford simulation results, with UGS density = 0.55, Blue fire point = 10 

cells, and 1 Blue shot/cycle: (c) Blue’s ability to prevent Red penetration into Blue 

territory and (d) Blue situational awareness of Red.................................................................67 

Figure 41. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 10 

cells, and 1 Blue shot/cycle: (a) combat unit survival and (b) Blue sensor coverage.............68 

Figure 42. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 10 

cells, and 1 Blue shot/cycle: (c) Blue’s ability to prevent Red penetration into Blue 

territory and (d) Blue situational awareness of Red.................................................................69 

Figure 43. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 17 

cells, and 1 Blue shot/cycle: (a) combat unit survival and (b) Blue sensor coverage.............71 

Figure 44. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 17 

cells, and 1 Blue shot/cycle: (c) Blue’s ability to prevent Red penetration into Blue 

territory and (d) Blue situational awareness of Red.................................................................72 
vi

Figure 45. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 10 

cells, and 3 Blue shots/cycle: (a) combat unit survival and (b) Blue sensor coverage...........73 

Figure 46. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 10 

cells, and 3 Blue shots/cycle: (c) Blue’s ability to prevent Red penetration into Blue 

territory and (d) Blue situational awareness of Red.................................................................74 

Figure 47. Fombler’s Ford simulation results, with UGS density = 1.00, Blue fire point = 10 

cells, and 1 Blue shot/cycle: (a) combat unit survival and (b) Blue sensor coverage.............76 

Figure 48. Fombler’s Ford simulation results, with UGS density = 1.00, Blue fire point = 10 

cells, and 1 Blue shot/cycle: (c) Blue’s ability to prevent Red penetration into Blue 

territory and (d) Blue situational awareness of Red.................................................................77 

Figure 49. Fombler’s Ford simulation results, with UGS density = 1.00, Blue fire point = 17 

cells, and 3 Blue shots/cycle: (a) combat unit survival and (b) Blue sensor coverage...........78 

Figure 50. Fombler’s Ford simulation results, with UGS density = 1.00, Blue fire point = 17 

cells, and 3 Blue shots/cycle: (c) Blue’s ability to prevent Red penetration into Blue 

territory and (d) Blue situational awareness of Red.................................................................79 

Figure 51. BSAR return map for the Fombler’s Ford simulation, with UGS density = 0.55, 

Blue fire point = 10 cells, and 1 Blue shot/cycle. ....................................................................81 

Figure 52. BSAR return map for both benign environment (blue squares) and IO-stressed 

(red crosses) Fombler’s Ford simulations, with UGS density = 0.75, Blue fire point = 10 

cells, and 1 Blue shot/cycle......................................................................................................81 

Figure 53. BSAR return map for both benign environment (blue squares) and IO-stressed 

(red crosses) Fombler’s Ford simulations, with UGS density = 1.00, Blue fire point = 17 

cells, and 3 Blue shots/cycle. ...................................................................................................82 

Figure A-1. Coevolutionary dynamics of firepower range classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit.....................88 

Figure A-2. Coevolutionary dynamics of single-shot Phit classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit.....................89 

Figure A-3. Coevolutionary dynamics of firepower strength classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit.....................90 

Figure A-4. Coevolutionary dynamics of firepower round limit classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit.....................91 

Figure A-5. Coevolutionary dynamics of armor strength classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit.....................92 

Figure A-6. Coevolutionary dynamics of sensor range classes with class population (y-axis) 

vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. .................................93 

Figure A-7. Coevolutionary dynamics of carried land mine classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit.....................94 

Figure A-8. Coevolutionary dynamics of carried RF jammer bomb classes with class 

population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit...95 

Figure A-9. Coevolutionary dynamics of mine/jammer bomb deployment probability with 

class population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) 

Red unit....................................................................................................................................96 
vii

Figure A-10. Coevolutionary dynamics of signature classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit........................................97 

Figure A-11. Coevolutionary dynamics of crew classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit........................................98 

Figure A-12. Coevolutionary dynamics of maximum speed classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit.....................99 

Figure A-13. Coevolutionary dynamics of transportation mode classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit...................100 

Figure A-14. Coevolutionary dynamics of attack initiation classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit...................101 

Figure A-15. Coevolutionary dynamics of friendly attraction/repulsion classes with class 

population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red 

unit. ........................................................................................................................................102 

Figure A-16. Coevolutionary dynamics of enemy attraction/repulsion classes with class 

population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red 

unit. ........................................................................................................................................103 

Figure A-17. Coevolutionary dynamics of friendly territory attraction/repulsion classes with 

class population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) 

Red unit..................................................................................................................................104 

Figure A-18. Coevolutionary dynamics of enemy territory attraction/repulsion classes with 

class population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) 

Red unit..................................................................................................................................105 

Figure A-19. Coevolutionary dynamics of agitation classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit......................................106 
viii

List of Tables 

Table 1. The GA configuration file used in the coevolutionary simulation. ................................40 

Table 2. The survival fitness configuration file used in the coevolutionary simulation...............40 

Table 3. The genome configuration file used in the coevolutionary simulation. .........................41 

Table 4. Definition of a set of combat agent classes based on sensor/firepower range 

combinations............................................................................................................................43 

Table 5. Definition of a set of combat agent classes based on behavioral gene weighting 

combinations............................................................................................................................45 

Table A-1. Combat agent classes based on gene G0 (firepower range)......................................107 

Table A-2. Combat agent classes based on gene G1 (single-shot Phit). ......................................107 

Table A-3. Combat agent classes based on gene G2 (firepower strength)..................................107 

Table A-4. Combat agent classes based on gene G3 (firepower number of rounds). .................107 

Table A-5. Combat agent classes based on gene G4 (armor strength)........................................107 

Table A-6. Combat agent classes based on gene G5 (sensor range). ..........................................108 

Table A-7. Combat agent classes based on gene G11 (carried land mines). ...............................108 

Table A-8. Combat agent classes based on gene G12 (carried RF jammer bombs)....................108 

Table A-9. Combat agent classes based on gene G14 (probability of mine/jammer bomb 

deployment in cell). ...............................................................................................................108 

Table A-10. Combat agent classes based on gene G15 (signature). ............................................108 

Table A-11. Combat agent classes based on gene G16 (number of human crews). ....................109 

Table A-12. Combat agent classes based on gene G17 (maximum speed)..................................109 

Table A-13. Combat agent classes based on gene G18 (mode of transportation). ......................109 

Table A-14. Combat agent classes based on gene G20 (probability of initiating an attack). ......109 

Table A-15. Combat agent classes based on a combination of genes G21 (friendly attraction) 

and G22 (friendly repulsion)...................................................................................................109 

Table A-16. Combat agent classes based on a combination of genes G23 (enemy attraction) 

and G24 (enemy repulsion).....................................................................................................109 

Table A-17. Combat agent classes based on a combination of genes G25 (friendly territory 

attraction) and G26 (friendly territory repulsion). ..................................................................110 

Table A-18. Combat agent classes based on a combination of genes G27 (enemy territory 

attraction) and G28 (enemy territory repulsion). ....................................................................110 

Table A-19. Combat agent classes based on gene G29 (probability of agitation).......................110 
ix

Executive Summary 

In this report, a model developed to analyze the emergent operational impact of hostile 

information operations (IO) stress events directed at a network-centric battle team is presented. 

In such a battle team, there is an operational triad of military roles that combat platforms can 

fulfill; they are as follows: 

• sensors that sample the battlespace to collect data on friendly platforms, enemy 

platforms, and environmental conditions, 

• commanders that assess situational data collected by sensors in order to decide upon 

responsive military actions, and 

• actors that execute the military actions dictated by commanders. 

This triad forms the basis of a distributed “system-of-systems” of battlefield entities connected 

via information flows, where each type of entity occupies a specific operational “niche.” It 

should be noted that the network-centric battle team is characterized by its extensive use of 

networks to share information and allow for a coherent picture of the battlefield to emerge within 

all networked battlefield entities. 

In order to simulate the operational dynamics of a military network-centric system-of-systems, 

the concept of agent-based models is introduced. In general, an agent is an autonomous 

computational entity with a perpetuated internal state and associated set of rules governing 

behavior. The agent’s state is usually represented as a dynamic vector describing metrics such as 

agent position, identity, current functionality, and so on. A colony of agents can interact with 

one another by passing messages between themselves, which can represent communication, 

cooperative actions, or conflict. Given these elements, an agent-based model is then a collection 

of interacting agents contained within a virtual “artificial world.” J. H. Holland1 describes seven 

basic attributes of complex adaptive systems that should be reflected in a multiagent model of a 

network-centric system-of-systems; they are aggregation, nonlinearity, information flows, 

diversity, interagent tagging, access to internal models, and the use of building blocks. Taken 

together, these attributes allow for the emergence of a combat unit of distributed agents 

(platforms) that individually boasts specific functional capabilities that can be collectively 

exploited by the unit via networked communications. 

An agent-based model of a network-centric system-of-systems can be implemented by 

incorporating local-rule-based agent dynamics patterned after cellular automata (CA) models. In 

general, a CA consists of a possibly infinite, n-dimensional regular lattice of cells. Each cell can 

 1 Holland, J. H. Hidden Order: How Adaptation Builds Complexity. Reading, MA: Perseus Books, pp. 10–37, 1995. 
x

be in a measurable state chosen from a finite alphabet (i.e., a set of possible cell states). The 

states of all cells in the lattice are updated simultaneously in discrete time steps using a transition 

function, which takes as its input the current state of the center cell and some finite collection of 

nearby cells that lie within a finite distance, collectively known as a “neighborhood.” Cell 

neighborhoods may be either fixed or variable over time (but fixed within a time step); these 

neighborhoods are usually defined to be local to a center reference cell, but can extend beyond 

this conventional limit if necessary. Within this context, agents can then be defined as mobile 

automata (with perpetuated internal states and autonomous/semiautonomous operational 

behaviors) that move throughout a CA lattice and communicate with each other via message 

passing. Note that this combination of cell neighborhoods and agents can effectively model an 

operational environment and the entities that inhabit that environment, respectively. 

The optimal configuration of individual agents within a combat unit (network-centric or 

otherwise) can be explored through the use of genetic algorithms (GA), a class of heuristic 

search methods that mimic and exploit the genetic dynamics underlying natural evolution to 

search for optimal solutions of general combinatorial optimization problems. Given an 

optimization problem, GAs provide a mechanism for efficiently exploring the problem solution 

space for “good solutions” based on one or more “goodness of fit” criteria called fitness 

functions. Possible solutions to the optimization problem of interest are encoded as 

chromosomes (or sometimes as a set of correlated chromosomes called a genome), where a 

chromosome is a string of problem-relevant variables or genes. The GA then evolves one 

population of chromosomes (or genomes) into another according to their fitness by employing an 

iterative “evolutionary” process. For the current application, the GA will need to coevolve the 

configurations of adversarial units of Blue and Red agents engaged in combat by simultaneously 

optimizing hardware-based capability and behavior assignment to individual agents in both units 

based on combat performance. 

The first step in building a CA-based combat agent unit or battle team is to define the attributes 

of a generic combat agent within the battle team. Within the context of a CA lattice, a combat 

agent is an autonomous or semiautonomous automaton with a dynamically-perpetuated internal 

functional state vector representing agent mobility, firepower, sensor, and communication 

capabilities, as well as a set of behavioral rules defining how an agent interacts with other 

combat agents operating within a shared two-dimensional (2-D) regular lattice. The combat 

agent state vector maintains and updates the following information fields: 

• mobility capability status, 

• firepower capability status, 

• on-board sensor functional parameters, 

• supply status of on-board unattended ground sensor (UGS) units, radio frequency (RF) 

battery-powered “jammer bombs,” and land mines, 
xi

• an internal sensor message database representing the combat agent’s situational awareness 

(SA) of the virtual battlespace, 

• interagent communication capability status (transmission and reception functionality), and 

• rules of behavior that define the degree of autonomy within the combat agent. 

Combat agent movement is then modeled as the mapping of this agent state vector into adjacent 

CA lattice cells during a subsequent simulation time cycle (i.e., a periodic sequence of events 

that occurs over a contiguous set of simulation time steps). 

In addition to a dynamic state vector, each combat agent also maintains a set of functional ranges 

associated with the following capabilities: 

• the maximum distance an on-board sensor can acquire information on neighboring combat 

agents, 

• the maximum distance a transmitting agent can communicate with receiving neighboring 

agents, 

• the maximum distance an agent with firepower capability can engage a neighboring agent, 

• the maximum distance a jammer-equipped agent can jam the communication reception 

capability of neighboring agents, and 

• the maximum distance an agent can move in one simulation time cycle. 

For purposes of simplification, these ranges define a set of concentric extended Moore 

neighborhoods relative to the 2-D CA lattice, where the reference combat agent occupies the 

center cell within the neighborhood. 

The next step in building the combat agent battle team is to design a framework with which to 

represent the capabilities and behaviors of the entire team. Invoking genetic algorithm 

terminology, the battle team genome represents the capability/behavioral configuration of the 

combat agent collective unit. Each genome is constructed of a set of chromosomes, where a 

chromosome represents the capability/behavioral configuration of a single combat agent. Each 

combat agent chromosome, in turn, is constructed from a set of genes representing individual 

agent capability and behavioral attributes. Finally, each gene is characterized by a finite set of 

alleles or permissible gene states. Note that although each combat agent chromosome within a 

battle team genome is constructed by using the same schema or gene template, the allele ranges 

for specific genes can vary from chromosome to chromosome. Once the genes within a generic 

combat agent schema have been specified, the rules governing how these genes are utilized by 

combat agents within a dynamic CA simulation can be delineated. 

Once the CA-based combat agent simulation engine has been designed, the development and 

demonstration of a software framework that utilizes the CA engine for IO-stressed Blue/Red 
xii

combat simulations can be discussed. Due to the prolonged runtimes necessary to simulate 

densely packed UGS networks and multihop message relaying within the CA implementation of 

combat agent communication nets, the decision was made to codevelop two different versions of 

the IO simulation software framework. 

In the first simulation framework, combat-motivated coevolution of both Blue and Red unit 

configuration genomes is simulated within an IO-stressed virtual environment. In this 

framework, the duration of an update time cycle within the CA simulation engine is compressed 

to facilitate the thousands of Blue/Red combat instances necessary to execute the genomic 

coevolution. Initial populations of Blue/Red candidate genomes (or candidate combat unit 

configurations) are randomly generated and then filtered using hardware cost constraints. These 

candidate genomes are then input into the combat_ga CA simulation engine along with a 

virtual landscape and a territorial importance gradient (that serves to motivate combat agent 

movement towards a goal), and a combat simulation is executed. Such combat simulations are 

subsequently run for each possible Blue/Red genome pairing (or a subset thereof). 

During a Blue/Red combat simulation, a data filter dynamically calculates the dynamic 

operational “fitness” (analogous to one or more combined combat unit measures of performance 

averaged over a combat mission time interval) of each opposing combat agent unit within the 

simulation. These combat simulations can include hostile IO actions (which, in this case, are 

limited to RF jamming by jammer-equipped combat agents). The output of the data filter is then 

either directed into a GA or can be directly analyzed in time series format. In the former case, 

the top five “fittest” genomes for each side are determined (based on the collective results from 

all instantiated Blue/Red combat simulations) and then used by the GA to create new “offspring” 

genome populations (through the use of algorithmic “genetic operations” performed on genome 

pairs working in combination with a cost filter). These replenished populations are used to 

continue the coevolutionary process across successive genome “generations”; this process is 

iteratively repeated until both Blue and Red genome fitness values converge at or near a maximal 

value. The genome populations can also be dynamically classified (as a function of hardwarebased capabilities and behaviors encoded into a genome) throughout a coevolutionary simulation 

run. 

Genomic combat fitness is the measure of performance used to guide the Blue/Red 

coevolutionary process. The fitness of a combat unit is an operational measure of its collective 

ability to successfully execute combat actions and is analogous to one or more combined combat 

unit measures of performance. Fitness is thus a metric that can be used to evaluate the 

operational effectiveness of a specific genomic configuration vector encoding of a combat unit’s 

hardware capabilities and behaviors. To carry this out, a set of genome fitness functions or 

dynamic measures of performance was designed. These functions are evaluated at the end of 

each simulation time cycle update of the CA engine and represent the collective performance of 
xiii

all agents within a combat unit. The linear superposition of all fitness function metrics used to 

measure the perpetuation of combat unit collective capabilities throughout a simulation is called 

the survival fitness, which provides an objective function that can be optimized by the GA. 

A coevolutionary Blue vs. Red simulation was run on a 32-CPU SGI Origin 3800 computer, with 

Blue and Red units programmed to execute defender and invader combat roles, respectively. 

Here, Blue/Red combat instances for each evolutionary generation of candidate genomes were 

run in parallel, after which calculated survival fitness values were centrally averaged and 

reported. Three combat simulation instances were executed (and then averaged) for each 

Blue/Red genome candidate pairing, with 30 pairings per generation. The latter situation was 

iteratively realized by randomly selecting one candidate genome from both Blue and Red 

genome pools, for a combat pairing, removing the selected genomes from their respective pools 

and repeating the random selection process until all genomes have been paired for combat. Each 

combat simulation was run for 300 time cycles, at which point values of the time-averaged 

survival fitness for both Blue and Red genomes were evaluated and reported. Then, for both 

Blue and Red units, the top five candidate genome scorers from each generation were used to 

produce the next generation of 30 candidate genomes via random mating pairs. 

Coevolutionary simulation results were collected across 1360 successive generations of Blue and 

Red combat unit genomes. In these results, Red quickly “discovers” unit configurations that 

serve to maintain its average fitness within the interval [0.38, 0.43], which, in turn, motivates 

Blue to compensate with new configurations that likewise drive average Blue fitness to the 

interval [0.49, 0.59]. This coevolutionary process nicely illustrates the “Red Queen Principle” 

from evolutionary biology, where for an evolutionary system, continuing development is needed 

just in order to maintain its fitness level relative to the systems it is coevolving with. Since, due 

to computational runtime constraints, the coevolutionary process was prematurely terminated 

before any strong evidence of survival fitness convergence was evidenced, it remains unclear 

whether the Blue and Red combat units would ever break out of the “Red Queen” oscillatory 

pattern illustrated in the figure. If the current theory of coevolutionary neutral mutations (where 

gene mutations produce insignificant variations in relative fitness) is applicable within the 

current context, then fitness convergence might never be achievable without relaxing some of the 

gene allele and cost constraints defined for this demonstration simulation. 

Evidence of continuing development of both Blue and Red combat units throughout the 

demonstration simulation is provided by a classification analysis of Blue and Red genome 

populations. This type of analysis involves first setting up sets of combat agent types or 

“classes” specified by agent chromosome gene values and then observing the correlated 

coevolutionary dynamics of optimal Blue and Red unit configurations relative to these sets of 

classes. Analysis results using a first set of agent classes (based on different combinations of 

sensor/firepower ranges) show that the Blue unit configuration is observed to oscillate between 

variable mixtures of medium- and long-range sensor agents with variable-range firepower 

weapon capability, finally settling into a mode where most agents have medium-range sensor and 
xiv

short-range firepower capability. The Red unit, on the other hand, which must execute the more 

operationally challenging primary role of invader, coherently explores sensor/firepower classes 

until finally settling on a medium-range sensor and short-range fire for most agents. 

Next, using a second set of combat agent classes specified by agent behavioral weighting 

combinations, Blue is seen to haphazardly explore various classes, then settle on a 

passive/indecisive behavioral mode for over 600 generations, and finally switch over to a 

mixture of passive/indecisive and aggressive/ indecisive agents at the end of the coevolutionary 

run. This is not surprising, in that Blue’s primary mission is defensive, and thus Blue agents can 

afford to be “indecisive” (effectively immobile) and less “aggressive” than Red agents. After an 

initial exploratory period, Red, on the other hand, first “discovers” a behavioral configuration 

where all agents act in an aggressive (firing a weapon without provocation) and noncovert 

(moving directly towards Blue agents) manner that serves to maintain its operational fitness. 

Then, when Blue counters with behaviors that stress Red fitness, Red, in turn, progressively 

adopts passive and noncovert, aggressive and noncovert, aggressive but indecisive (trying to 

move both towards/away from Blue), and aggressive but covert modes of behavior, finally 

settling on the last mode (with a small fraction of the Red unit continuing to explore other 

behaviors). Again, Red faces the greater evolutionary pressure since its primary mission is 

invasive in nature and thus benefits by coherently moving towards better strategic behavioral 

modes. 

In the second IO simulation software framework, a quasi-scripted (and thus nonevolving) combat 

agent C2 process is directly embedded into the combat_proto CA simulation engine. This 

second and enhanced version of the CA simulation engine models the deployment and function 

of notional internetted UGS units and multihop message relaying and thus extends the networkcentric capabilities of combat_ga. In this new framework, hand-scripted configuration 

genomes were constructed in order to represent a Blue unit of networked mobile platforms 

making up a network-centric combat unit as well as a Red unit of loosely organized 

nonnetworked platforms. Combat simulations were run using the combat_proto CA engine 

in combination with routines to generate a simulation landscape (based on the notional 

“Fombler’s Ford” terrain designed for the Future Combat Systems concept illustration), a 

territorial importance gradient superimposed over the landscape, and to initially position Blue 

and Red combat agents. Then, a data filter dynamically calculated preselected fitness functions 

of each opposing combat agent unit within the simulation, which can include hostile IO actions. 

Finally, the fitness function outputs of the filter were analyzed in a time series format (as 

opposed to weighting the functions and then combining them into the survival fitness objective 

function as was done in the previous framework). 

A sensitivity analysis was conducted using the combat_proto CA engine in conjunction with 

the “Fombler’s Ford” scenario in order to explore the sensitivity of the following model 

parameters to IO stress: 
xv

• UGS density - the average packing density of the peripheral Blue UGS network. 

• Blue fire point - the outward distance from the perimeter of Blue territory at which point 

Blue combat agents are allowed to fire on acquired Red targets. 

• Blue shots/cycle - the number of shots a firepower-equipped Blue combat agent can fire 

during one simulation time cycle. 

This sensitivity analysis was predicated on the time series response generated by the following 

four particular fitness functions as applied to specific combat units: 

• Blue and Red combat unit survival levels, 

• Blue’s ability to sense the battlefield, 

• Blue’s ability to prevent Red penetration into Blue territory, and 

• collective Blue situational awareness of Red. 

In this set of simulations, these fitness functions serve as dynamic measures of collective combat 

unit performance. 

Time series results from six different “Fombler’s Ford” scenario variants (run in both benign and 

IO-stressed operational environments) were generated in the sensitivity analysis. In these 

scenario variants, the analyzed model parameters were set to values within the following ranges: 

(1) UGS density = 0.55 – 1.00, (2) Blue fire point = 10 ! 17 cells, and (3) Blue shots/cycle 

= 1 ! 3. Multiple simulation instances of each scenario variant were run using the set of 20 

sequential random seeds {0, 1, 2, ..., 19}, and then time series results were averaged and plotted. 

The time series results clearly illustrated a (not surprising) monotonically increasing combat 

effectiveness and survivability of the Blue unit as the model parameters were increased from 

minimal to maximal values. The results also clearly illustrated that the amount of operational 

degradation experienced the Blue unit in the presence of Red jammer bombs was increasingly 

attenuated as a function of increasing model parameter values. Thus, the sensitivity analysis 

demonstrated how providing the Blue unit with a combination of enhanced operational 

capabilities could act to significantly attenuate the perturbative impact of a form of IO stress that 

results in denial of communication network connectivity. 

Given that the combat agents used within the CA models are reactive in nature (with limited 

decision-making capability and no explicit “commander” type of agent able to formulate new 

tactical strategies when operationally required), the next step in model development is to provide 

agents with more deliberative behaviors, thus providing a framework for the hierarchical C2 

decision-related structures ubiquitous to all military combat units. Current research into building 

more complex varieties of decision-making military command agents as well as hostile terrorist- 
xvi

style agents operating within densely populated urban areas can provide a foundation for 

designing deliberative combat agents. Further work is still required, however, in order to realize 

rapidly adaptive agents operating within a network-centric combat structure. 
1

1. Introduction 

1.1 Purpose 

The cellular-automata-based combat agent model presented in this report was developed in order 

to analyze the emergent behavior exhibited by a network-centric battle team exposed to hostile 

information operations (IO) stress in the form of radio frequency (RF) communications jamming. 

1.2 Background 

1.2.1 Network-Centric Complex Adaptive Systems 

A complex system can be defined as a group of many interacting parts functioning as a unified 

whole. Such a system is distinguishable from its environment by recognizable boundaries, 

where the global behavior of the whole system is generally not equal to the sum of the parts. 

Even if the individual behavior of an isolated part is linear, the collective behavior that emerges 

globally through networked interactions and interdependencies among the parts is often 

nonlinear. This collective behavior arises from the detailed structure, behavior, and interpart 

relationships as they are defined on a finer scale, all of which dynamically evolve within the 

context of an irreducible whole. Furthermore, a complex adaptive system can then be defined as 

a complex system whose interacting parts can adapt to changing operational environments. 

The paradigm of complex adaptive systems is directly applicable to a relatively new concept in 

military operations enabled by advances in information technologies known as network-centric 

warfare (NCW). Perhaps the best way to define the concept of NCW is through listing its basic 

tenets (Herman and Hayes 1999) as follows: 

• Higher military echelons provide objectives, timelines, intent, and resource planning. 

• Bottom-up self-synchronizing execution allows all weapons and sensors to be available to 

all subscribers on the same or linked networks. 

• A high level of shared situational awareness enables execution decisions to be coordinated 

without significant upper echelon intervention. 

These tenets suggest the following operational triad of military entities (Alberts et al. 2000): 

• sensors that sample the battlespace to collect data on friendly platforms, enemy platforms, 

and environmental conditions, 

• commanders that assess situational data collected by sensors in order to decide upon 

responsive military actions, and 

• actors that execute the military actions dictated by commanders. 
2

This triad is illustrated in Figure 1, which depicts a distributed “system-of-systems” of battlefield 

entities connected via information flows, where each type of entity occupies a specific 

operational “niche.” It should be noted that NCW is not characterized as warfare by networks, or 

against networks but rather as the extensive use of networks to share information and allow for a 

coherent picture of the battlefield to emerge within all networked battlefield entities. 

Figure 1. Triad of distributed battlefield entity types within a network-centric “system-of-systems.” 

To analyze a network-centric “system-of-systems” as a complex adaptive system operating 

within an equally-complex IO environment, the following three distinct scales define the 

structure, behavior, and relationships: (1) the microscale, which addresses interactions and 

interdependencies between information system (IS) components (e.g., 1553B data bus, combat 

net radios, and personal computers) within a platform, (2) the mesoscale, which addresses local 

interactions/interdependencies between networked battlefield platforms, and (3) the macroscale, 

which addresses the holistic global behavior of the collective system-of-systems structure (zum 

Brunnen et al. 2000). Usually, IO threat/target system interactions occur at the micro- and/or 

mesoscale levels, affecting information flows between IS components within a platform and/or 

flows between locally networked platforms, respectively. In the current model, the emergent 

global or macroscale impact of IO stress, which is directed at the mesoscale networked platforms 

making up a combat unit or “battle team,” is analyzed. 
3

1.2.2 Agent-Based Models 

In order to simulate the operational dynamics of a military network-centric complex adaptive 

system, the concept of agent-based models is introduced next. In general, an agent (see Figure 2) 

is an autonomous computational entity with a perpetuated internal state and associated set of 

rules governing behavior (Maes 1990). The agent’s state is usually represented as a dynamic 

vector describing metrics such as agent position, identity, current functionality, and so on. A 

colony of agents can interact with one another by passing messages between themselves, which 

can represent communication, cooperative actions, or conflict. Given these elements, an agentbased model is then a collection of interacting agents contained within a virtual “artificial world” 

(see Figure 3). Agent interactive behavior is dictated by sets of if/then “production rules,” which 

are often specific to different agent types and define strategies for dealing with other agents as 

well as a possibly stressful operating environment. Also, agents are often organized into some 

type of hierarchical organization. Finally, the time-series output of an agent-based model can be 

viewed directly via a graphical user interface (GUI) or processed through a data filter. 

Figure 2. Representation of a generic agent. 

Holland (1995) describes the following seven basic attributes common to all complex adaptive 

systems that thus should be reflected in any multiagent model of such systems: 

(1) aggregation, which involves the categorization of information at the agent level and 

emergent behavior at the macroscopic multiagent level, 

(2) nonlinearity, where the whole is often more or less than the sum of the parts, 

(3) information flows between interacting agents, 
4

Figure 3. Representation of a generic agent-based model. 

(4) diversity among agents (i.e., multiagent systems are heterogeneous), 

(5) tagging to identify individuality among agents, 

(6) internal models that specify how agents react to other agents and/or the operational 

environment, and 

(7) building blocks, which are the components or primitives used to construct agents. 

Conceptually, an agent-based model of a network-centric system-of-systems would need to 

demonstrate each of these attributes, where distributed agents (platforms) individually boast 

specific functional capabilities that can be collectively exploited by the combat unit via 

networked communications. Existing agent-based models of land warfare such as Irreducible 

Semiautonomous Adaptive Combat (ISAAC)(Ilachinski 2000), Simulation of Information in 

Battlefield Decisions (SinBaD) (Hencke 1998), and AgentKit (Woodaman 2000) all address the 

emergent behavior of combat units of interacting Blue and Red agents, but neglect the 

aforementioned network-centric “sensor/commander/actor” operational triad. What is still 

needed is a model where agents collect and fuse situational awareness data from distributed 

sensory devices and then execute subsequent combat actions based on these data. 

On a design level, an agent-based model of a network-centric spatially-dispersed combat unit 

would need to address both the semiautonomous dynamics of individual agents as well as the 

adaptive behavior of such agents (which is driven by the necessity for an agent to adapt to the 

adaptation patterns of other agents). This model structure can be implemented by incorporating 

the following: 
5

• local-rule-based agent dynamics patterned after cellular automata models and 

• parameterization of the local decision space and the formulation of strategy and/or tactics 

patterned after genetic algorithm models. 

These two types of models will be discussed in the next subsections. 

1.2.3 Cellular Automata 

In general, a cellular automata (CA) (von Neumann 1966) consists of a possibly infinite, ndimensional regular lattice of cells. Each cell can be in a measurable state chosen from a finite 

alphabet (i.e., a set of possible cell states). The states of all cells in the lattice are updated 

simultaneously in discrete time steps using a transition function, which takes as its input the 

current state of the center cell and some finite collection of nearby cells that lie within a finite 

distance, collectively known as a “neighborhood.” Figure 4 displays two well-known CA cell 

neighborhoods in two dimensions, where C = center cell and N = neighbor cell. Cell 

neighborhoods may be either fixed or variable over time (but fixed within a time step); these 

neighborhoods are usually defined to be local to a center reference cell but can extend beyond 

this conventional limit if necessary. Within this context, agents can then be defined as mobile 

automata (with perpetuated internal states and autonomous/semiautonomous operational 

behaviors) that move throughout a CA lattice and communicate with each other via message 

passing. Note that this combination of cell neighborhoods and agents can effectively model an 

operational environment and the entities that inhabit that environment, respectively. 

Figure 4. CA cell neighborhoods in two dimensions: (a) von Neumann neighborhood 

and (b) Moore neighborhood. 

1.2.4 Genetic Algorithms 

Genetic algorithms (GA) are a class of heuristic search methods that mimic and exploit the 

genetic dynamics underlying natural evolution to search for optimal solutions of general 

combinatorial optimization problems (Mitchell 1993). Given an optimization problem (e.g., 

maximizing a function over a specific interval), GAs provide a mechanism for efficiently 

exploring the problem solution space for “good solutions” based on one or more “goodness of 
6

fit” criteria called fitness functions. Possible solutions to the optimization problem of interest are 

encoded as chromosomes (or sometimes as a set of correlated chromosomes called a genome), 

where a chromosome is a string of problem-relevant variables or genes. The GA then evolves 

one population of chromosomes into another according to their fitness by employing an iterative 

“evolutionary” process (Figure 5). This process involves the following iterative steps (Ilachinski 

1996): 

• Step 1: Begin with a randomly generated population of chromosome- (or genome-) 

encoded “solutions” to a given combinatorial optimization problem. 

• Step 2: Calculate the fitness of each chromosome/genome, where fitness is a measure of 

how well a member of the population performs at solving the problem. 

• Step 3: Retain only the fittest members of the chromosome/genome population and discard 

the remaining members. 

• Step 4: Generate a new population of chromosomes/genomes from the remaining members 

of the old population by applying the genetic operations reproduction (the generation of a 

“child” chromosome/genome from two high-fitness “parent” chromosomes/genome), 

crossover (cutting two parent chromosomes into two subchromosomes per parent or cutting 

two parent genomes into two sets of chromosomes or subgenomes per parent and then 

mixing and reconnecting the subchromosomes or subgenomes to create two new children 

chromosomes or genomes, respectively) and mutation (randomly changing the gene values 

within a chromosome). 

• Step 5: Calculate the fitness of these new members of the chromosome/genome population, 

retain the fittest members, discard the remaining members, and reiterate the process until a 

“maximally fit” solution is achieved. 

Figure 5. Iterative evolutionary processes within a genetic algorithm (from 

Ilachinski 1996). 
7

This evolutionary process is often referred to as navigating through a fitness landscape, which is 

an n + 1-dimensional hypersurface that measures the fitness of all possible n-dimensional 

chromosome/genome configurations. Thus, convergence of a genetic algorithm towards an 

optimal chromosome/genome configuration is analogous to moving towards a maximal fitness 

point on the fitness landscape. 

There have been several previous research efforts to apply GA technology to military 

optimization problems. Of these, the most relevant relative to an agent-based modeling 

application is the FOX & HARE model, which addresses the optimization of behavioral courseof-action (COA) selection within two opposed and coevolving Blue and Red military units 

(Hillis and Winkler 2000; Funes and Pollack 2000). In a FOX & HARE simulation, Blue and 

Red unit COAs were generated by genetic algorithms, with relative COA fitness evaluated via 

unit performance in a simplified wargame model. Then, new Blue and Red unit COAs were 

generated and played against each other, thus allowing new Blue/Red solutions to coevolve 

during each generational iteration. However, although it will address coevolving Blue/Red 

military units of agents, the model addressed in this report will focus on optimization of 

hardware-based capability and behavior assignment to individual agents rather than COA 

optimization as handled by a unit commander. 

1.2.5 Threat 

The threats addressed within this report are hostile IO stress events (U.S. Department of the 

Army 1996) that result in denial of communication network service within a network-centric 

battle team. 

1.3 Scope 

The applicability of the CA agent-based model presented in this report is constrained by the 

following assumptions: 

• Networked platform nodes within a network-centric battle team treat all acquired 

situational awareness (SA) data as true and valid (i.e., combat actions based on the relative 

validity of data are not modeled). 

• Human-based command and control (C2) of combat agents is very limited and thus is 

restricted to the criterion that “unmanned” combat agents may fire upon acquired enemy 

targets provided that the SA target data they use are provided to them by “manned” combat 

agents. 

• IO stress will only impact a node’s ability to receive SA messages from other neighboring 

nodes and not affect internal information flows within a platform node. 

• All combat agents are modeled as reactive in nature, where each agent decision process 

maps to one, and only one, possible output. 
8

2. The Cellular Automata-Based Combat Agent Model 

2.1 The Cellular Simulation System 

The CA-based combat agent model is implemented through the use of the Cellular simulation 

system, an easy-to-use toolkit for the modeling of physical systems with cellular automata. The 

Cellular system consists of several modules as follows: 

• a programming language, Cellang, and associated compiler, cellc,

• an abstract virtual cellular automata machine (avcam) for Cellang code execution, and 

• cellview, a program for viewing simulation results in either two-dimensional (2-D) 

sections or in three dimensions with variable perspective. 

Compiled Cellang programs can be run with input provided at any specified time during the 

execution. The results of an execution can either be viewed graphically as an output stream of 

cell locations and values or passed through a custom filter before being reported. The output 

stream, or a similar stream produced by a custom filter, can also be directed into cellview for 

viewing or may be passed through other programs that compile statistics, massage the data, or 

merely act as a valve to control the flow of data from the cellular automata program to the 

viewer. 

Programs written in Cellang have two main components—a cell description and a set of 

statements. The cell description determines how many dimensions there are, the vector of fields 

associated with each cell, and the range of values that can be associated with each field. In 

addition to cell-specific fields, the cell description also specifies the various types and attribute 

fields of mobile agents that can reside in the cells. The set of statements, on the other hand, 

encode the transition rules for all lattice cells (which can be applied homogeneously to all cells 

or heterogeneously according to cell type or the type of agent[s] currently residing in the cell). 

For more detailed information on the Cellular toolkit, see Eckart (1992a, 1992b). 

2.2 Building the Combat Agent Battle Team 

2.2.1 The Generic Combat Agent 

The first step in building a CA-based combat agent unit or battle team is to define the attributes 

of a generic combat agent within the battle team. Within the context of a CA lattice, a combat

agent is an autonomous or semiautonomous automaton with a dynamically perpetuated internal 

functional state vector representing agent mobility, firepower, sensor, and communication 

capabilities, as well as a set of behavioral rules defining how an agent interacts with other 

combat agents operating within a shared 2-D regular lattice. A representation of a generic 
9

combat agent is depicted in Figure 6. As illustrated in this depiction, a combat agent state vector 

maintains and updates the following information fields: 

• mobility capability status, 

• firepower capability status, 

• on-board sensor functional parameters, 

• supply status of on-board unattended ground sensor (UGS) units, RF battery-powered 

“jammer bombs,” and land mines, 

• an internal sensor message database representing the combat agent’s SA of the virtual 

battlespace, 

• interagent communication capability status (transmission and reception functionality, and 

• rules of behavior that define the degree of autonomy within the combat agent. 

Combat agent movement is then modeled as the mapping of this agent state vector into adjacent 

CA lattice cells during a subsequent simulation time cycle (i.e., a periodic sequence of events 

that occurs over a contiguous set of simulation time steps). 

In addition to a dynamic state vector, each combat agent also maintains a set of functional ranges 

associated with the following capabilities: 

• the maximum distance an on-board sensor can acquire information on neighboring combat 

agents, 

• the maximum distance a transmitting agent can communicate with receiving neighbor 

agents, 

• the maximum distance an agent with firepower capability can engage a neighbor agent, 

• the maximum distance a jammer-equipped agent can jam the communication reception 

capability of neighboring agents, and 

• the maximum distance an agent can move in one simulation time cycle. 

For purposes of simplification, these ranges define a set of concentric extended Moore 

neighborhoods relative to the 2-D CA lattice (see Figure 4b), where the reference combat agent 

occupies the center cell within the neighborhood. Also, the range hierarchy depicted in Figure 6 

is but one realization of permissible configurations (in other words, the ranges are flexible 

relative to one another). 
10

Figure 6. A generic combat agent. 

2.2.2 Combat Agent Battle Team Representation 

The next step in building the combat agent battle team is to design a framework with which to 

represent the capabilities and behaviors of the entire team. Figure 7 illustrates this representation 

framework. Invoking the genetic algorithm terminology first introduced in section 1.2.4, the 

battle team genome represents the capability/behavioral configuration of the combat agent 

collective unit. Each genome is constructed of a set of chromosomes, where a chromosome 

represents the capability/behavioral configuration of a single combat agent. Each combat agent 

chromosome, in turn, is constructed from a set of genes representing individual agent capability 

and behavioral attributes. Finally, each gene is characterized by a finite set of alleles or 

permissible gene states. Note that, although each combat agent chromosome within a battle team 

genome is constructed by using the same schema or gene template, the allele ranges for specific 

genes can vary from chromosome to chromosome. 

Figure 7. Representation framework for a combat agent battle team 

configuration. 
11

Given a viable battle team framework within which to encode agent hardware capabilities and 

behaviors, the specific genes within a combat agent chromosome can now be assigned. The 

genetic schema template for encoding combat agent capability and behavioral states is shown in 

Figure 8. Within this schema, there are hardware-based capability classes addressing combat 

agent firepower, passive self-protection, on-board sensor, communications, on-board RF jammer, 

carried supplies of UGS units, mines, and RF jammer bombs, and mobility. Also included is a 

characterization of the combat agent’s acquisition signature, as well as a behavioral “personality 

vector” encoding the battlefield tendencies of the agent to attack, retaliate, and move 

towards/away from friends/enemies and or friendly/enemy territory. 

Figure 8. The genetic schema representing hardware and behavioral attributes of a generic combat agent. 

2.3 Combat Agent Dynamics 

Once the genes within a generic combat agent schema have been specified, the rules governing 

how these genes are utilized by combat agents within a dynamic CA simulation can be 

delineated. In the following subsections, these CA transition rules (involving chromosome genes 

within the capability, signature, and behavioral classes) are discussed in detail. 

2.3.1 Firepower 

The firepower weapon maintained by a combat agent is defined by maximum weapon range 

(gene G0), single-shot probability of hitting an engaged target (gene G1), fired projectile 

“strength” (gene G2), and the number of rounds remaining of the initial number of rounds (gene 

G3). The weapon dynamics are described by the following pseudocode: 
12

IF target range < G0 AND random%1.00 < G1 AND remaining rounds > 0 THEN 

 remaining rounds = remaining rounds – 1 

 IF target armor strength < G2 THEN 

 target is killed 

 ELSE IF target armor strength > G2 THEN 

 target armor strength = target armor strength – G2

 target survives encounter 

 END 

ELSE 

 weapon is not fired 

END. 

Here, random is a random whole number, so that random%1.00 represents a random fraction 

sampled from the range [0, 1.0]. In other words, the firing combat agent will kill an engaged 

target based on a probability of hit Phit = G1 given that (a) the target is within firing range, (b) the 

engaging combat agent has at least one remaining round left, and (c) the target’s armor strength 

is less than the engaging weapon strength G2. In all other cases, the target will survive the 

encounter. 

2.3.2 Passive Self-Protection 

Combat agents are self-protected by conventional passive (as opposed to active) armor 

characterized by the initial armor strength G4. Then, upon engagement, the combat agent’s 

armor strength is reduced by the engaging agent’s firepower strength until the residual armor 

strength < 0 (at which point, an engaged combat agent “dies”). 

2.3.3 On-Board Sensor 

The single on-board sensor (representing some type of notional multimodal sensor suite) 

maintained by a combat agent is defined by maximum sensor range (gene G5), probability of 

target detection (i.e., the likelihood of sensing a neighboring agent when it is present [gene G6]), 

and probability of false alarm (i.e., the likelihood of reporting an agent’s presence when it is 

really not present [gene G7]). In addition, if the neighboring combat agent is an enemy of the 

sensing agent, then the acquisition signature of the target agent (gene G15 [see section 2.3.7]) is 

also utilized. During a simulation time cycle, each combat agent that remains “alive” or 

functional sends out a “health status” message reporting its CA lattice location and identity (i.e., 

Blue or Red). This status message is then “sensed” by a sensor-equipped combat agent as 

described by the following pseudocode: 
13

IF distance to sensed entity < G5 AND random%1.00 < G6 AND ((sensed entity is an 

enemy of sensing agent AND random%1.00 < G15) OR sensed entity is an ally of sensing 

agent) THEN 

 acquire “health status” message from sensed entity 

END. 

Again, random%1.00 represents a random fraction sampled from the range [0, 1.0] so that an 

entity is sensed with probability of detection Pdetect = G6 given that it is within the sensing agent’s 

sensor range. 

At the same time, the sensing combat agent’s sensor may generate a false positive as described 

by the following: 

IF G5 > 0 AND random%1.00 < G7 THEN 

acquire self-generated false “health status” message with randomly selected CA 

lattice location and sensed entity identity 

END. 

Here, the sensor will generate a false “health status” message (that the sensing agent believes to 

be correct data) with probability of false alarm Pfalse alarm = G7 given that the sensor range > 0. 

2.3.4 Communications 

Combat agent intercommunication is defined by the maximum communication range (gene G8). 

This range reflects the transmitter power limitations of a message-transmitting combat agent so 

that a receiving combat agent will be able to accept a transmitted message given that (a) the 

receiver lies within the extended Moore CA neighborhood of size (2*G8 + 1) × (2*G8 + 1) cells 

relative to the transmitter agent and (b) the receiving agent is not currently being jammed. The 

communication capability is employed only between allied combat agents in order to share SA 

data messages (which report the identities and positions of both friendly and enemy agents) 

acquired by sensor-equipped agents. 

2.3.5 On-Board RF Jammer 

Combat agent on-board communications jamming capability is defined by the maximum 

jamming range (gene G9). As with communication, this range reflects the RF power limitations 

of a jamming combat agent, so that a receiving combat agent will not be able to accept messages 

from another transmitting agent given that the receiver lies within the extended Moore CA 

neighborhood of size (2*G9 + 1) by (2*G9 + 1) cells relative to the jamming agent. This notional 

on-board jammer is assumed to be able to disrupt transmissions equally across all RF 

communication bands. 
14

2.3.6 Supplies of UGS Units/Mines/Jammer Bombs 

The supply and deployment of UGS units, land mines, and RF jammer bombs carried by a 

combat agent are defined by the numbers of carried UGS units, land mines, and jammer bombs 

(the initial numbers of which are encoded in genes G10, G11, and G12, respectively), maximum 

UGS deployment range (gene G13), and the probability of deploying a UGS/mine/jammer bomb 

unit in a currently unit-free lattice cell (gene G14). For land mines and jammer bombs, the 

maximum deployment range is assumed to be equal to zero (i.e., a combat agent can only 

emplace a mine or jammer bomb into the lattice cell the agent currently occupies, where the 

mine or bomb will remain after the agent chooses to move on at some future time). The overall 

deployment process is described by the following pseudocode: 

IF number of remaining UGS units > 0 AND random%1.00 < G14 THEN 

 number of remaining UGS units = number of remaining UGS units − 1 

 place a UGS unit into a cell if the distance to the target cell < G13

END. 

IF number of remaining land mines > 0 AND random%1.00 < G14 THEN 

 number of remaining land mines = number of remaining land mines − 1 

 place a land mine into the current cell 

END. 

IF number of remaining jammer bombs > 0 AND random%1.00 < G14 THEN 

 number of remaining jammer bombs = number of remaining jammer bombs − 1 

 place a jammer bomb into the current cell 

END. 

It should be noted that although the probability of deploying a unit into a unit-free cell is the 

same for UGS, land mine, and jammer bomb units, the decisions to deploy each type of unit are 

executed independently of each other. Also, it is assumed that UGS units, land mines (which 

impact combat agents only), and jammer bombs (which do not affect a UGS unit’s sensing 

capability) will not functionally interfere with each other if placed within the same cell. 

In the following subsections, the specific functional dynamics of UGS units, land mines, and RF 

jammer bombs are described in detail. 

2.3.6.1 UGS Units 

Within the context of the current CA model, a UGS unit is a set of fields that is attached to a cell, 

representing the placement of a notional autonomous sensor onto a ground location within the 

battlespace (Followill et al. 1997; Haider 1998). A combat agent transmits a UGS unit to a target 

cell by sending a “UGS missile” message to the cell. This message represents a notional missile 
15

packed with one or more individual UGS units on board, which are dispersed to fill a 

neighborhood surrounding the target cell once the “UGS missile” message reaches its target. 

Then, upon reaching a cell (or neighborhood of cells), the “UGS missile” message maps the 

UGS fields UGS range, UGS owner (i.e., Blue or Red), and UGS transmit/receive functionality

(for internetted data sharing) into that cell. As discussed in section 2.3.6, the size of the extended 

Moore CA neighborhood that a combat agent may fire “UGS missile” messages to is defined by 

chromosome gene G13. The size of the neighborhood surrounding a UGS target cell, however, is 

preset within the CA code (see section 3.2.3 for an example of UGS deployment via “UGS 

missile” messages). 

Once a UGS unit has been positioned in a CA lattice cell (where it will remain for the duration of 

a simulation run), the unit begins to function as a remote sensor. Figure 9 illustrates the 

operational dynamics of a UGS unit. In Figure 9a, the UGS unit senses its local environment, 

which consists solely of the lattice cell it occupies. Then, in Figure 9b, as a Red combat agent 

moves into the cell and is sensed by the UGS unit, an SA data message that reports the x/y lattice 

position and identity (i.e., Blue or Red) of the sensed agent is transmitted to the adjacent 

north/south/east/west cells making up a von Neumann CA neighborhood. Finally, if these 

adjacent cells are occupied by allied internetted UGS units that are currently unjammed, the 

messages are received and retransmitted across new von Neumann neighborhoods surrounding 

the relaying UGS units. In this manner, SA reports can propagate across a network or “web” of 

UGS units, thus allowing sensor-equipped combat agents with limited sensor range to access SA 

information from local nodes within the UGS web. 

2.3.6.2 Land Mines 

Unlike UGS units, land mines can only be deployed into the lattice cell currently occupied by a 

dispensing combat agent. This is done by directly attaching the characteristic land mine 

parameter field mine owner alliance (i.e., Blue or Red) to the cell occupied by the mine. This 

parameter serves a twofold purpose—it identifies the battlefield alliance of the dispensing agent 

(1) for combat agent damage purposes (see the following) and (2) to prevent other agents from 

the same alliance from dropping additional mines into the cell. Once deployed, the land mine 

dynamics are described by the following pseudocode: 

 IF a combat agent enters the land mine cell AND combat agent alliance ≠ mine owner 

alliance AND random%1.00 < 0.5 THEN 

 Reset agent chromosome gene G17 (maximum speed) = 0 

 END. 
16

Figure 9. Operational dynamics of a UGS unit: (a) UGS unit senses 

its local environment and (b) UGS unit transmits an SA 

report on the sensed agent across a von Neumann CA 

neighborhood. 

Thus, if a combat agent enters a lattice cell also occupied by a land mine previously planted there 

by another agent belonging to the enemy alliance relative to the first agent, there is a 50% 

probability that the entering combat agent will lose its mobility capability (by setting G17 = 0 

within the agent’s chromosome). Here, the assumption is made that combat agents represent 

mobile hardware platforms as opposed to infantry so that land mines result in degraded agent 

capability as opposed to death. It is also assumed that a land mine will not affect the mobility of 

agents belonging to the same battlefield alliance responsible for first planting the mine. Finally, 

it is assumed that each land mine is actually a cluster of bomblets spread across a lattice cell so 

that a land mine continues to function (essentially perpetually) after multiple detonations. 

2.3.6.3 RF Jammer Bombs 

As with land mines, RF jammer bombs can only be deployed into the lattice cell currently 

occupied by a dispensing combat agent. Again, this is done by directly attaching the 

characteristic jammer bomb parameter fields bomb owner alliance and total jammer bomb 

lifetime (in simulation time cycles) to the cell occupied by the bomb. The first bomb parameter 

is used to prevent other members of the same alliance as the bomb-dispensing agent from 

dropping additional jammer bombs into the cell, while the second parameter is used to set the 

lifetime of the battery powering the jammer bomb (which is decremented by 1 once every 

simulation time cycle). Once deployed, a jammer bomb acts to jam the communication reception 
17

capability of all combat agents (irregardless of battlefield alliance) and internetted UGS units 

within a predefined extended Moore CA neighborhood given that the residual bomb’s lifetime 

> 0. Figure 10 depicts the jamming neighborhood of a jammer bomb whose jamming range 

= 2 (this parameter can be adjusted within the CA code). 

Figure 10. The extended Moore CA neighborhood 

surrounding a notional RF jammer bomb. 

2.3.7 Signature 

The acquisition signature of a combat agent is represented as the probability that the agent will 

be detected by an enemy combat agent equipped with an on-board sensor (gene G15). As 

described in section 2.3.3, this gene works in conjunction with a sensor-equipped enemy agent to 

determine whether the identity and position of the sensed combat agent is indeed acquired. It 

should be noted that, since UGS units are constrained to sense only a very limited local 

environment (i.e., a single lattice cell), these types of sensors are assumed to acquire targets 

irregardless of the target agent’s signature gene allele value. 

2.3.8 Human-Based Attributes 

Within the context of combat agents, the functional role of human soldiers (which can be thought 

to occupy a combat agent platform) is simply to constrain the engagement opportunities of 

“unmanned” combat agents. This type of constraint is implemented via gene G16, which encodes 

the number of human crews within a combat agent. Basically, the constraint allows an 

unmanned agent (where G16 = 0) to fire upon an acquired target only when the target SA data has 

been supplied to the unmanned agent by another allied agent with one or more human crews 

(where, for the latter agent, G16 > 0). Although meant to suggest human-based control of all 

target engagements, the cognitive capabilities of combat agents in the current model are purely 

reactive in nature. Thus, the only impact of gene G16 on combat unit effectiveness is either to (a) 

force the unit to rely heavily on its allied communication network (when most agents in the unit 

are unmanned) or (b) force the unit configuration to evolve to a point where most agents are 

manned (and thus operate autonomously with minimal interagent cooperation). 
18

2.3.9 Mobility 

Combat agent mobility is characterized by a maximum allowable speed (gene G17) and mode of 

transportation (gene G18). Since combat agents are allowed to move (either north, south, east, or 

west) one cell length per simulation time cycle at most, relative agent speed reflects the 

probability that an agent will move one cell length during that time cycle. Thus, the probability 

of agent movement during the n

th simulation time cycle is 

( ) ( ) , G

v t

P t

17

n

move n = (1) 

where |v(tn)| is the agent “speed” (the magnitude of the velocity v) during the n

th time cycle. 

Possible modes of transportation include (a) ground, wherein agents must move around “terrain 

impediment” cells added to the CA lattice (representing impassible mountains, rivers, and 

buildings), and (b) air, wherein agent movement is unrestricted by terrain. Combat agent 

movement is then described by the following pseudocode: 

IF G17 > 0 AND random%1.00 < current agent speed/G17 THEN 

 IF G18 = ground THEN 

 IF terrain in direction of intended movement is passable THEN 

 move one cell in direction of intended movement 

 ELSE IF terrain is impassible THEN 

 rotate 180° and then move in that new direction 

 END 

 ELSE IF G18 = air THEN 

 move one cell in direction of intended movement 

 END 

ELSE 

 remain in the current cell 

END. 

Calculation of the direction of intended combat agent movement is described in detail in the next 

section. 
19

2.3.10 Behavior 

Combat agent behavior is encoded into a vector within the agent chromosome representing the 

“personality” of that agent. In the following subsections, modes of combat agent behavior that 

define combat engagement actions, goal-driven mobility, and agitation-driven mobility are 

discussed. 

2.3.10.1 Combat Engagement Actions 

Engagement actions demonstrated by a combat agent are characterized by (a) the probability that 

the combat agent will initiate a firepower attack against an enemy agent Pattack (gene G20) and (b) 

the probability that the agent will retaliate against an enemy once attacked Pretaliate (gene G19). 

The application of Pattack and Pretaliate to instantiating a combat action is described by the 

following pseudocode: 

IF G0 (firepower range) > 0 AND number of remaining firepower rounds > 0 AND a 

combat agent has acquired and selected a target to engage THEN 

IF distance between engaging combat agent and target < G0 AND random%1.00 

< Pattack THEN 

 fire one round at target 

 END 

IF agent has just received fire AND distance between engaged agent and attacking agent 

< G0 AND random%1.00 < Pretaliate THEN 

 fire one round at attacking agent 

 END 

END. 

Here, G0 = 0 indicates a combat agent without firepower capability, and the number of remaining 

firepower rounds < G3. Also, a combat agent will select to engage the first target agent it 

acquires (i.e., the agent reported in the first queued SA message within the agent’s internal 

message database) provided that (a) the target is within engagement range and (b) the engaging 

agent perceives the target agent as an enemy. 

2.3.10.2 Goal-Driven Mobility 

Combat agent mobility is motivated by spatially-oriented goal points towards which agents are 

coded to either approach or avoid. These goal points are of two basic types: (1) the center of 

mass (CM) position of a cluster of combat agents and (2) a mission-related territorial objective 
20

location within the CA lattice. Here, the first goal point type is dynamic (i.e., a function of 

collective combat agent speed and direction), whereas the second type is preassigned to a lattice 

cell (or a local cluster of contiguous cells) and thus remains static throughout a simulation. 

In movement motivated by the first type of mobility goal point, a combat agent is either 

attracted, repulsed, or both attracted and repulsed by the CM of the unit to which the agent 

belongs (i.e., the friendly combat unit) as well as the CM of the enemy force (i.e., the foe combat 

unit). An agent’s attraction to and repulsion from the friendly unit CM is based on weighting 

factors (within the range [0, 100]) that are stored in chromosome genes G21 and G22, respectively. 

Likewise, the agent’s attraction to and repulsion from the foe unit CM is based on similar 

weighting factors that are stored in chromosome genes G23 and G24, respectively. Employing 

these attraction/repulsion weights as encoded in genes G21 through G24 of the i

th combat agent’s 

chromosome, the x and y lattice coordinates of the superimposed friendly and foe CMs relative to 

that agent are as follows: 

( ) ( ) ( ) ( )

( ) G G x () () t x t ,

CM t G G x t x t

foes

foe n agent n

agent

24

agent

23

friends

friend n agent n

agent

22

agent

n 21

agent

x

i

i i

i

i i i

∑

∑

− ∗ −

= − ∗ − +

 (2) 

and 

( ) ( ) ( ) ( )

( ) G G y () () t y t ,

CM t G G y t y t

foes

foe n agent n

agent

24

agent

23

friends

friend n agent n

agent

22

agent

n 21

agent

y

i

i i

i

i i i

∑

∑

− ∗ −

= − ∗ − +

 (3) 

respectively, where xfriend(tn) and yfriend(tn) are the x and y lattice coordinates at the n

th time cycle, 

respectively, of a neighboring agent friendly to the i

th agent; xfoe(tn) and yfoe(tn) are the x and y

lattice coordinates at the n

th time cycle, respectively, of a neighboring agent hostile towards the 

i

th agent; and xagenti

(tn) and yagenti(tn) are the x and y lattice coordinates at the n

th time cycle, 

respectively, of the i

th agent itself. Thus, equations 2 and 3 calculate the net combined 

friendly/foe combat unit CM location relative to the i

th agent by summing over the difference in 

position of all other friendly and enemy agents from the position of the i

th agent, multiplying 

these sums by the net attraction (which can be less than 0, indicating a repulsion) of the i

th agent 

towards friendly and foe unit CMs, and then adding the results. 

In movement motivated by territorial objective goal points, a combat agent is either attracted, 

repulsed, or both attracted and repulsed by a mission-related territorial importance gradient that 

defines the relative mission importance of a lattice cell (or local cluster of contiguous cells) 

relative to a predefined objective point within the lattice. In the current model implementation, 

an importance gradient is assigned to territory “owned” or controlled by either a Blue or Red 
21

combat unit (i.e., Blue or Red territory, respectively), where each gradient has a maximallyimportant lattice location representing combat unit “headquarters” towards which an enemy 

agent might want to approach (or avoid) and a set of contiguous minimally-important locations 

representing unclaimed neutral territory. As with movement driven by combat unit CMs, a 

combat agent’s attraction to and repulsion from both the friendly and foe headquarter locations 

(i.e., the mission objective points) is based on weighting factors that are stored in chromosome 

genes G25 through G28, respectively. Employing these attraction/repulsion weights, the x and y

lattice coordinates of a new type of CM based on the i

th agent’s reaction to local territorial 

subgradients within both friendly and foe importance gradients are as follows: 

( )

( ) ( )

( ) ( ) ∑















=

− ∗ ∗ −

=

− ∗ ∗ −

=

neighborhood(t ) cell cell agent n

agent

28

agent

27

cell cell agent n

agent

26

agent

25

n

territory

x

n i

i i

i

i i

i

when cell.owner !agent.alliance

G G Importance x x (t )

when cell.owner agent.alliance

G G Importance x x (t )

CM t , (4) 

and 

( )

( ) ( )

( ) ( ) ∑















=

− ∗ ∗ −

=

− ∗ ∗ −

=

neighborhood(t ) cell cell agent n

agent

28

agent

27

cell cell agent n

agent

26

agent

25

n

territory

y

n i

i i

i

i i

i

G G Importance y y (t )

G G Importance y y (t )

CM t

when cell.owner !agent.alliance

when cell.owner agent.alliance , (5) 

respectively, where Importancecell is the local value of a territorial importance gradient within a 

lattice cell (i.e., an integer ranging from 0 up to 128), xcell and ycell are the x and y lattice 

coordinates, respectively, of the lattice cell under examination, and xagenti(tn) and yagenti(tn) again 

are the x and y lattice coordinates at the n

th time cycle, respectively, of the ith agent. Here, 

equations 3a and 3b calculate the superposition of the friendly and foe local importance 

subgradient CM locations relative to the i

th agent by multiplying the difference in position of 

neighboring subgradient cells from the position of the ith agent by the importance of each 

subgradient cell and the net attraction of the i

th agent towards friendly and foe subgradient CMs 

and then summing these results over all cells within a preset subgradient neighborhood. An 

important distinction is made between subgradient cells owned by the ith agent’s friends (in 

which case, the logical relation cell.owner = agent.alliance evaluates to TRUE) vs. cells owned 

by foes (in which case, the logical relation cell.owner ! = agent.alliance evaluates to TRUE). 

Once the agent cluster and importance subgradient CM locations have been calculated for the ith 

agent, they are combined into a total net CM location according to the following: 

, i i i territory

x

agent

x

total CM x = α∗CM + β ∗CM (6) 
22

and 

, i i i territory

y

agent

y

total CM y = α∗CM + β ∗CM (7) 

where α and β are weighting constants representing the relative degree to which the i

th agent 

desires to move toward (or avoid) friendly/enemy agent clusters vs. friendly/enemy territorial 

objective points.*

 Thus, i total CM x and i total CM y provide the x and y coordinates, respectively, 

towards which the i

th agent intends to move during the next simulation time cycle. 

In addition to deciding upon a next direction of travel, a combat agent will also adjust its speed 

of movement relative to the total CM coordinates described in equations 6 and 7 (where this net 

CM is a function of both dynamic agents and static territory). This speed adjustment is set as a 

function of the “acceleration” of the i

th agent at the nth time cycle 

( ) ( ) ,

2

agent n

n n 1

total

y

total

x

agent n velocity t

t t

CM CM

∆speed t i

i i

i − −

∗ =

−

 (8) 

where tn − tn-1 is the duration of one simulation time cycle (i.e., one unit) and |velocityagenti

(tn)| 

is the magnitude of the agent velocity (i.e., the speed) at the n

th time cycle. Since agent speed is 

more akin to the probability of moving a fixed distance per time step rather than a true physical 

speed, the “acceleration” proposed in equation 8 is actually an artificial construct that can be 

used to maintain the position of the i

th agent as close as possible to the total CM location 

throughout a simulation (i.e., by adjusting agent speed so that ( ) ( )1 / − − ∗ n n

total

y

total

x CM CM t t i i

 

= |velocityagenti

(tn)|2 or ∆speedagenti(tn) = 0). Thus, the new speed of the i

th agent at the (n+1)th

time cycle is given by the following: 

( )

( ) ( )

( )

() ()

 ( )













>

− ≤ −

<

+ ≥

+ = ,

and speed t 0

speed t 1 when ∆speed t ∆speed

and speed t speed

speed t 1 when ∆speed t ∆speed

speed t

agent n

agent n agent n min

agent n max

agent n agent n min

agent n 1

i

i i

i

i i

i (9) 

where ∆speedmin defines a minimal threshold value of ∆speedagenti

(tn) that triggers a speed 

adjustment when achieved or surpassed and speedmax is the maximum allowable agent speed. 

Both ∆speedmin and speedmax are adjustable but must remain fixed upon commencement of a 

simulation run. Interestingly, the algorithms described in equations 2–9 model a variety of agent 

“flocking” behavior that was first demonstrated by Reynolds in his Boids model (Reynolds 

1987). 

 * In the current model implementation, these weights are fixed at α = β = 0.5, representing equal prioritization between agentand territorial-oriented mobility goals. 
23

2.3.10.3 Agitation-Driven Mobility 

In addition to the goal-motivated movement discussed in section 2.3.10.2, combat agents can 

also be driven to move by “agitation.” An agitated agent is thus characterized by Pagitation, the 

probability that the agent will randomly select and pursue a new direction of travel (while 

maintaining the same speed as calculated using equations 8 and 9) per simulation time cycle 

(gene G29). The application of Pagitation to instantiate random movement within a combat agent is 

described by the following pseudocode: 

IF random%1.00 < Pagitation THEN 

 new direction = random%4 

 IF new direction = 0 THEN 

 move north 

 ELSE IF new direction = 1 THEN 

 move east 

 ELSE IF new direction = 2 THEN 

 move south 

 ELSE IF new direction = 3 THEN 

 move west 

 END 

END. 

It should be noted that an agitated agent will revert back to the nonagitated state at the start of 

each new time cycle (and will again move randomly based on Pagitation), and thus the behavioral 

dynamics of agitated movement is modeled as a random process with no memory. 

3. Combat Agent Simulations 

Now that the elements of the CA simulation engine have been described, the development and 

demonstration of a software framework that utilizes the CA engine for IO-stressed Blue/Red 

combat simulations can be discussed. Due to the prolonged runtimes necessary to simulate 

densely-packed UGS networks and multihop message relaying within the CA implementation of 

combat agent communication nets, the decision was made to codevelop two different versions of 

the IO simulation software framework. In the first framework (section 3.1), combat-motivated 

coevolution of both Blue and Red unit configuration genomes is simulated within an IO-stressed 
24

virtual environment. In this framework, the duration of an update time cycle within the CA 

simulation engine is compressed to facilitate the thousands of Blue/Red combat instances 

necessary to execute the genomic coevolution. In the second framework (section 3.2), the CA 

engine time cycle duration is expanded to allow for the simulation of UGS networks and 

multihop message relaying previously mentioned. 

3.1 Blue/Red Unit Combat-Driven Coevolution 

3.1.1 Genomic Coevolution Framework 

The first software framework was developed in order to explore the dynamics of two opposing 

but coevolving units of networked autonomous combat agents (Figure 11). Within this 

framework, “friendly” (Blue) and “foe” (Red) agents are encoded within the combat unit 

configuration genome introduced in section 2.2.2, where the metrics within a genome represent 

notional agent hardware-based capabilities and combat-oriented behaviors. In this context, the 

genome represents an instantiation of a possible configuration of networked mobile platforms 

making up a network-centric combat unit. Initial populations of Blue/Red candidate genomes (or 

candidate combat unit configurations) are randomly generated by the random_genome routine 

working in combination with a cost filter (see section 3.1.5). These candidate genomes are then 

input (via the place routine) into the combat_ga CA simulation engine, along with a virtual 

landscape (generated by the terrain routine) and a territorial importance gradient (generated by 

the territory routine), and a combat simulation is executed. Such combat simulations are 

subsequently run for each possible Blue/Red genome pairing (or a subset thereof). 

During a Blue/Red combat simulation, a data filter dynamically calculates the dynamic 

operational “fitness” (analogous to one or more combined combat unit measures of performance 

averaged over a combat mission time interval) of each opposing combat agent unit within the 

simulation (the functions used to calculate dynamic fitness are described in detail in section 

3.1.4). These combat simulations can include hostile IO actions (which, in this case, are limited 

to RF jamming by jammer-equipped combat agents). The output of the data filter is then either 

directed into a GA (i.e., the reproduce routine) or can be directly analyzed in time series 

format. In the former case, the top five “fittest” genomes for each side are determined (based on 

the collective results from all instantiated Blue/Red combat simulations) and then used by the 

GA to create new “offspring” genome populations (through the use of algorithmic “genetic 

operations” performed on genome pairs [section 3.1.4] working in combination with the cost 

filter previously mentioned). These replenished populations are used to continue the 

coevolutionary process across successive genome “generations;” this process is iteratively 

repeated until both Blue and Red genome fitness values converge at or near a maximal value. 

The genome populations can also be dynamically classified (as a function of hardware-based 

capabilities and behaviors encoded into a genome) throughout a coevolutionary simulation run 

by the classify routine (for a demonstration of this routine, see section 3.1.6.2). 
25

The Blue/Red coevolutionary process described in the previous paragraphs is executed by 

running the Evolve Perl script. There are two other available routines for running coevolution 

simulations that are functionally redundant with Evolve (indicated by the “||” symbol at the top 

of Figure 11) but can also take advantage of parallel processing computer hardware capability 

when available. The first of the redundant routines is parAvgEvolve, a Perl script identical to 

Evolve except that each simulation instance of a Blue/Red genome pairing is partitioned and run 

on different parallel processor threads. Then, results from multiple instances of the genome 

pairing are averaged in parallel. The second of the redundant routines is parGenEvolve, another 

Perl script identical to Evolve except that now a sequence of nonpartitioned simulation instances 

of a specific Blue/Red genome pairing is assigned to a processor, and then instance sequences 

covering different genome pairings are run in parallel. After that, the results from each 

sequence run are averaged on the respective processor, and averaged results from all genome 

pairings are centrally collected. 

Figure 11. Software framework for simulating Blue/Red combat unit genomic 

coevolution. 

3.1.2 The combat_ga CA Simulation Engine 

Written in Cellang (see section 2.1), the combat_ga CA simulation engine (so named to 

indicate its specific design for implementation within the GA-based coevolutionary simulation 

framework) models the interaction between two opposing units of combat agents. The 

operational dynamics of this simulation engine are cyclic in design, where a repetitive sequence 

of actions is executed in parallel for each cell within the CA universe during each successive 

simulation time cycle. This sequence is depicted in Figure 12, which illustrates the following 

cycle of processes (where a process can unfold over one or more sequential simulation time 

steps): 
26

• Combat agents move into an adjacent cell within their local von Neumann neighborhood 

based on their goal-driven movement decisions (see section 2.3.10.2). This process is 

executed over one time step. 

• Combat agent communication reception is jammed by neighboring enemy agents equipped 

with on-board RF jamming capability (i.e., gene G9 within the enemy agent chromosome 

> 0 [see section 2.3.5]). This process is executed over one time step, which runs 

concurrently with the first time step of the subsequent process discussed next. 

• Messages transmitted from combat agents to neighboring agents are propagated across the 

CA universe. These messages can include (a) acquired data traveling from sensed entities 

to sensor-equipped agents, (b) sensor data shared amongst allied agents over their 

communication network, and (c) firepower payloads moving from an engaging agent 

towards an acquired target. This process is executed over nine sequential time steps. 

• Engaged targets react to received fire (i.e., either die or survive engagement with reduced 

armor capability). This last process is executed over one time step. 

Figure 12. Cyclic dynamics of the combat_ga CA simulation engine. 

Thus, one entire cycle of simulation processes unfolds over 11 sequential time steps, which are 

iteratively repeated throughout a combat simulation instance. 

The third process or phase within the combat_ga simulation time cycle depicted in Figure 12 

involves the propagation of messages between stationary combat agents positioned in cells 

located anywhere across the CA lattice. In order to minimize the duration of the message 

propagation phase, an intelligent relay network (IRN) algorithm was developed to handle 

message delivery to receiving agents. This algorithm essentially builds a series of relay stations 

into the underlying fabric of the CA universe, where the relays are assigned to cells and are 

distributed in a regular and uniform manner on a rectangular grid throughout the CA lattice. 
27

The collective relay network is “intelligent” in that it guarantees that each cell within the CA 

lattice only receives one copy of any particular message by the end of the message propagation 

phase. 

Figure 13 provides a demonstration of an IRN implemented within a limited 12 × 12 cell CA 

lattice. In this implementation, the lattice is partitioned into 16 nonoverlapping Moore 

neighborhoods, where the center cell within each neighborhood functions as a local relay station. 

In the northwest quadrant of the CA lattice is a transmitter combat agent (represented by a “T”) 

that launches a message intended for all neighboring receiver agents (represented by an “R”). 

This message is disseminated to all receiving agents in five successive phases. First, from time 

steps tn to tn+1, a copy of the message is transmitted horizontally from the T cell to the center cell 

of the right-hand adjacent Moore neighborhood. Next, from time steps tn+1 to tn+2, copies of the 

message are again transmitted horizontally from the T cell and the previous message destination 

site to the center cells of the left- and right-hand adjacent Moore neighborhoods, respectively. 

In the third phase, from time steps tn+2 to tn+3, copies of the message are transmitted vertically

downward from the center cells within the four Moore neighborhoods in the second row (each of 

which contains either the original message or a copy thereof) to the center cells of adjacent lower 

Moore neighborhoods. Following this, from time steps tn+3 to tn+4, copies of the message are 

transmitted both vertically upward and downward to the center cells of adjacent Moore 

neighborhoods. Finally, from time steps tn+4 to tn+5, copies of the message are transmitted from 

the center cells within all 16 Moore neighborhoods (each of which again contains either the 

original message or a copy thereof) to those intraneighborhood cells that contain receiver agents. 

In general, given an IRN consisting of M symmetric neighborhoods (where M must be an even

number) with N cells per neighborhood (and thus a symmetric CA lattice with M*N total cells)*

the following occurs: 

• the first “horizontal transmission” and second “vertical transmission” phases unfold over 

M 1⁄2 /2 time steps, and 

• the third and final “intraneighborhood transmission” phase unfolds over one time step. 

Given that the IRN within the combat_ga simulation engine was designed with M = 64 (8 

horizontal × 8 vertical neighborhoods) and N = 289 (a symmetric neighborhood of 17 horizontal 

× 17 vertical cells), the entire message propagation process unfolds over M 1⁄2 + 1 = 9 time steps. 

 *

 The symmetric neighborhoods depicted in Figure 13 happen to be of the nine-cell Moore type but can be made larger as the 

size of the CA lattice increases. The downside of this is that the degree of maximally allowed CA lattice parallelization scales 

inversely with neighborhood size N so that P ∝ N –1, where P = maximum number of parallel computational threads the CA 

lattice may be assigned to. 
28

Figure 13. Demonstration of the intelligent relay network as implemented within 

combat_ga.
29

A subtle point that needs to be further discussed is the superposition of different processes that 

occur during the message propagation phase depicted in Figure 12. There is an inherent 

sequential causality in the three message propagation processes, where (1) propagation of signals 

from sensed entities to sensor-equipped combat agents is followed by (2) sensor data sharing 

over a communication net, and finally (3) the exchange of fire between engagers and targets. 

Since the combat_ga CA engine was purposefully designed to minimize simulation run times, 

a causal sequence of events involving processes 1–3 can actually unfold over three successive 

time cycle updates. Since combat agents are allowed to move one cell at the start of every time 

cycle, an engaged target could be two cells removed from the location fired at by an agent acting 

on data received during a previous time cycle. To work around this problem, tags are used to 

link fired payloads with acquired targets. A tag is simply a unique positive integer assigned to 

each Blue and Red combat agent for identification purposes (see section 1.2.2). The following 

pseudocode describes the algorithm used by combat_ga to ensure that payloads are delivered 

to targeted agents: 

IF a fired round has reached a targeted cell THEN 

 delta_x = agent x position – target x position 

when agent x position > target x position 

 = target x position – agent x position otherwise 

 delta_y = agent y position – target y position 

when agent y position > target y position 

 = target y position – agent y position otherwise 

IF agent tag = target tag AND delta_x + delta_y < 2 THEN 

IF current agent armor strength < round fire strength THEN 

 engaged agent is killed 

 ELSE 

 engaged agent’s new armor strength = 

current agent armor strength – round fire strength 

 END 

 END 

END. 
30

Thus, the fired round essentially searches a symmetric diamond-shaped CA neighborhood 

centered on the target cell and then delivers the fire to an agent whose tag matches the target tag 

encoded into the round. 

3.1.3 Genomic Operational Fitness 

In Figure 11, the data filter is shown to dynamically calculate the operational fitness of each 

opposing combat agent unit within a combat_ga simulation (and then either directly output the 

time series fitness data or route it into the GA). The “fitness” of a combat unit is an operational 

measure of its collective ability to successfully execute combat actions and is analogous to one 

or more combined combat unit measures of performance. Fitness is thus a metric that can be 

used to evaluate the operational effectiveness of a specific genomic configuration vector 

encoding of a combat unit’s hardware capabilities and behaviors. To carry this out, a set of 

genome fitness functions or dynamic measures of performance was designed. These functions 

are evaluated at the end of each simulation time cycle update of the CA engine and represent the 

collective performance of all agents within a combat unit. The linear superposition of all fitness 

function values at the n

th time cycle tn is called the survival fitness. 

 Fsurv(tn) = W1F1(tn) + W2F2(tn) + W3F3(tn) + ... + WN FN (tn), (10) 

where F1(tn), F2(tn), F3(tn), ..., FN (tn) are the values of the N fitness functions at tn, W1, W2, W3, 

..., WN are fitness function weights (where Σi Wi

 = 1), and 0 < Fi(tn) < 1 for all functions. The 

survival fitness (so-named for its representation of perpetuated unit performance under stress) 

thus provides an objective function that can be optimized by the GA. 

Figure 14 illustrates the process that the data filter uses to evaluate Fsurv(tn) across a Blue/Red 

combat simulation time window. For a combat simulation unfolding over M discrete time 

cycles, values of Fsurv(tn) are sampled at the end of each time cycle and then summed and 

normalized to the time window length tM. This results in the time-averaged survival fitness 

〈Fsurv〉, where 0 < 〈Fsurv〉 < 1. It is this last measure that finally defines the relative fitness of 

both Blue and Red unit genome candidates within a multigenerational coevolutionary simulation. 

By constructing a multidimensional genomic hypersurface (representing combat interactions 

between different Blue and Red genome candidates), where each point on the hypersurface is 

weighted by Blue and Red values of 〈Fsurv〉 associated with the corresponding combat 

interaction, the GA has a “fitness landscape” which it can navigate in its search to cooptimize 

both Blue and Red unit operational fitness. 

In the following subsections, the individual fitness functions making up Fsurv(tn) are discussed 

in detail. In these fitness functions, the terms “friendly” and “enemy” are used to distinguish 

between combat agents within the same unit vs. agents within an adversarial unit, respectively. 
31

Figure 14. Evaluation of collective combat unit time-averaged survival fitness 〈Fsurv〉. 

Thus, the terms are relative to a specific unit and can be used in the context of either Blue or Red 

combat agents (e.g., the Blue unit is the “enemy” of the Red unit, while Red agents are all 

members of the same “friendly” unit). 

3.1.3.1 F1(tn): Prevent Enemy Penetration Into Friendly Territory 

The first fitness function measures the degree to which a unit of defensive combat agents 

prevents enemy agents from penetrating into the defender’s battlefield territory throughout the 

course of a simulation. The function is expressed as 

( )

( )

, impor tan ce

impor tan ce

1

max initial alive enemies

enemies in cell

friendlycells

friendly cell

k

k t

F t

n

1 n ∗

∗

= −

∑

 (11) 

where importancefriendly cell is the territorial importance of a defender-owned lattice cell 

(quantified as a number between 0 and 128), kenemies in cell(tn) is the number of enemy agents in the 

defender-owned lattice cell at the n

th simulation time cycle, importancemax is the maximum 

possible territorial importance of a cell (where importancemax > 0), and kinitial alive enemies is the total 

number of enemy agents within the simulation at the start of a simulation instance (where kinitial 

alive enemies > 0). The numerator of F1(tn) is calculated by summing values of the product 

importancefriendly cell*kenemies in cell(tn) over all defender-owned cells within the CA lattice. This 

fitness function utilizes the territorial importance gradient introduced in section 2.3.10.2 and 

ranges from a value of 1 (when all defender territory is clear of invasive enemy agents) to 0 

(when all enemy agents are still alive and clustered at the most “important” cell within the 

defender unit’s territory). 
32

3.1.3.2 F2(tn): Penetrate Into Enemy Territory 

The second fitness function measures the degree to which a unit of invasive combat agents 

successfully penetrates into an enemy’s battlefield territory throughout the course of a 

simulation. The function is expressed as 

( )

( )

, impor tan ce

impor tan ce

max initial alive friends

friends in cell

enemycells

enemy cell

k

k t

F t

n

2 n ∗

∗

=

∑

 (12) 

where importanceenemy cell is the territorial importance of an enemy-owned lattice cell (quantified 

as a number between 0 and 128), kfriends in cell(tn) is the number of friendly invasive agents in the 

enemy-owned lattice cell at the n

th simulation time cycle, importancemax is the maximum possible 

territorial importance of a cell (where again importancemax > 0), and kinitial alive friends is the total 

number of friendly invasive agents within the simulation at the start of a simulation instance 

(where kinitial alive friends > 0). Similar to F1(tn), the numerator of F2(tn) is calculated by summing 

values of the product importanceenemy cell*kfriends in cell(tn) over all enemy-owned cells within the 

CA lattice. This fitness function also utilizes the territorial importance gradient and ranges from 

a value of 0 (when all enemy territory is clear of invasive friendly agents) to 1 (when all friendly 

agents are still alive and clustered at the most “important” cell within the enemy unit’s territory). 

3.1.3.3 F3(tn): Friendly Combat Unit Survival 

The third fitness function measures the fraction of friendly combat agents within an allied unit 

that remain alive during the course of a simulation. The function is expressed as 

( ) ( ) ,

initial alive friends

alive friends

3 k

k t F t n

n = (13) 

where kalive friends(tn) is the number of friendly agents within an allied unit that remain alive at the 

n

th simulation time cycle and kinitial alive friends is the same as was defined in equation 9 (where 

again kinitial alive friends > 0). It should be noted that all agents have the same weighting relative to 

kalive friends(tn) (i.e., a value of 1 given that the agent is alive) irregardless of the specific 

capabilities or combat role that any particular agent might have. 

3.1.3.4 F4(tn): Enemy Combat Unit Casualties 

The fourth fitness function measures the fraction of enemy combat agents within an adversarial 

unit that are killed during the course of a simulation. The function is expressed as 

( ) ( ) ,

initial alive enemies

dead enemies

4 k

k t F t n

n = (14) 

where kdead enemies(tn) is the number of enemy agents within an adversarial unit that have been 

killed by the n

th simulation time cycle and kinitial alive enemies is the same as was defined in equation 
33

11 (where again kinitial alive enemies > 0). As is the case with F3(tn), all agents contributing to F4(tn) 

have the same weighting relative to kdead enemies(tn) (i.e., a value of 1 given that the agent has been 

killed) irregardless of the specific capabilities or combat role that any particular agent might 

have. 

3.1.3.5 F5(tn): Sense the Battlefield 

The fifth fitness function measures the fraction of the total CA universe collectively sensed by all 

sensor-equipped agents (in combination with stand-alone UGS units when available) within a 

combat unit during the course of a simulation. The function is expressed as 

( ) ( )

,

total cells

sensed cells

5 k

k t F t n

n = (15) 

where ksensed cells(tn) is the number of lattice cells within the CA universe collectively sensed by a 

combat unit during the n

th simulation time cycle and ktotal cells is the total number of cells within 

the CA universe. It should be noted that this fitness function only measures whether a cell has 

been sensed and not the degree to which the sensed information is shared amongst agents within 

the combat unit. 

3.1.3.6 F6(tn): Jam Enemy Communications 

The sixth fitness function measures the fraction of combat agents within a unit whose 

communication reception capability has been jammed by agents within an opposing unit during 

the course of a simulation. The function is expressed as 

( ) ( )

( ) ,

alive enemies

jammed enemies

6

n

n

n k t

k t

F t = (16) 

where kjammed enemies(tn) is the number of enemy agents whose communication reception has been 

jammed by an opposing unit of friendly agents during the n

th simulation time cycle and 

kalive enemies(tn) is the total number of surviving enemy agents evaluated at the same time cycle. In 

the case where kalive enemies(tn) = 0, F6(tn) is set equal to 1, it should be noted that communications 

jamming is accomplished either by jammer-equipped combat agents or by stand-alone jammer 

bombs (when available for deployment). 

3.1.3.7 F7(tn): Prevent Enemy Penetration Into Friendly Territory With Dynamic 

Renormalization 

The seventh fitness function is identical to F1(tn) (i.e., prevent enemy penetration into friendly 

territory) except that the denominator in the second term is now adaptively renormalized to the 

population of surviving enemies at a simulation time cycle rather than the initial enemy agent 

population. The function is expressed as 
34

( )

( )

( ) , impor tan ce

impor tan ce

1

max alive enemies

friendly cell enemies in cell

7

n

n

friendly cells

n k t

k t

F t

∗

∗

= −

∑

 (17) 

where all variable definitions are the same as in equation 11 except for kalive enemies(tn), which is 

the same as defined in equation 16. As with F6(tn), F7(tn) is set equal to 1 when kalive enemies(tn) 

= 0. This dynamic renormalization provides evolutionary pressure that forces the defensive 

combat unit to prevent any degree of enemy unit penetration into friendly territory throughout 

the duration of a simulation instance (i.e., friendly unit fitness is minimized as long as there is at 

least one surviving enemy agent and all surviving enemies penetrate into the defensive unit’s 

most important territory). 

3.1.3.8 F8(tn): Penetrate Into Enemy Territory With Dynamic Renormalization 

The eighth fitness function is identical to F2(tn) (i.e., penetrate into enemy territory) except that 

the denominator in the second term is now adaptively renormalized to the population of 

surviving friends at a simulation time cycle rather than the initial friendly agent population. The 

function is expressed as 

( )

( )

( ) , impor tan ce

impor tan ce

max alive friends

enemy cell friends in cell

8

n

n

enemy cells

n k t

k t

F t

∗

∗

=

∑

 (18) 

where all variable definitions are the same as in equation 12 except for kalive friends(tn), which is the 

same as defined in equation 13. Here, F8(tn) is set equal to 0 when kalive friends(tn) = 0. This 

dynamic renormalization provides evolutionary pressure that forces the invasive combat unit to 

infiltrate enemy territory at almost any casualty cost to the invaders (i.e., friendly unit fitness is 

maximized as long as there is at least one surviving friendly agent and all surviving friends 

penetrate into the enemy’s most important territory). 

3.1.3.9 F9(tn): Collective Friendly Situational Awareness of Friends 

The ninth fitness function measures the fraction of surviving combat agents within a unit whose 

current situational status (i.e., location, identity, and capability status) is known by all other 

agents within the unit (due to network-centric sensor data sharing via the unit communication 

network). The function is expressed as 

( )

( )

[ ] ( ) , 2

alive friends

all cells

friendly SA messages concerning friends

9

n

n

n

k t

m t

F t

∑

= (19) 

where mfriendly SA messages concerning friends(tn) is the number of SA data messages residing in a cell, 

which were broadcast by friendly agents reporting on other sensed friends within the combat unit 

at the n

th simulation time cycle, and kalive friends(tn) is the same as defined in equation 13. As with 
35

F8(tn), F9(tn) is set equal to 0 when kalive friends(tn) = 0. Since nonpropagating SA messages are 

associated with combat agents and each agent has at most one copy of an SA message reporting 

on a specific friendly agent, dividing the total number of friendly agent reports (collected 

throughout all cells within the CA universe) by the square of the number of friendly agents 

measures the degree of collective friendly situational awareness within a combat unit. 

3.1.3.10 F10(tn): Collective Friendly Situational Awareness of the Enemy 

The 10th and final fitness function measures the fraction of surviving combat agents within an 

enemy unit whose current situational status (i.e., location, identity, and capability status) is 

known by all agents within the opposing friendly unit (due to network-centric sensor data sharing 

via the friendly unit communication network). The function is expressed as 

( )

( )

() () ,

alive friends alive enemies

friendly SA messages concerning enemies

10

n n

all cells

n

n k t k t

m t

F t

∑= (20)

where mfriendly SA messages concerning enemies(tn) is the number of SA data messages residing in a cell, 

which were broadcast by friendly agents reporting on sensed enemies within the opposing 

combat unit at the n

th simulation time cycle, and kalive friends(tn) and kalive enemies(tn) are the same as 

defined in equations 13 and 16, respectively. Here, F10(tn) is set equal to 0 when kalive friends(tn) 

= 0 and is set equal to 1 when kalive enemies(tn) = 0. Similar to the case with F9(tn) regarding 

friendly agent awareness, dividing the total number of friendly agent reports regarding enemy 

agents (again collected throughout all cells within the CA universe) by the product of the number 

of surviving friendly and enemy agents allows F10(tn) to measure the degree of collective friendly 

unit situational awareness of the enemy. 

3.1.4 Genetic Operations 

As described in section 3.1.1, the GA instantiated within the reproduce routine acts to 

generate new combat unit configuration genome candidates by operating upon the fittest “parent” 

genomes (based on the preselected fitness functions introduced in the previous section). There 

are two different types of genetic operators built into the GA code; they are as follows: 

• crossover - a process that exchanges sets of contingent chromosome vectors between two 

parent genomes, and 

• point mutation - a process that randomly changes the allele values of individual genes 

within a chromosome. 

An example illustrating the application of these genetic operators to two parent genomes is 

depicted in Figure 15. In this example, a point within the crossover variance is selected within 

each parent chromosome. The crossover variance indicates the preset positional range within a 

genome measured outward from the center point where the genome can be separated into two 
36

Figure 15. Genetic operations on two candidate combat unit genomes. 

sets of constituent chromosomes.* These chromosome sets (indicated in the figure by the white 

and gray shadings) are exchanged between candidate genomes nos. 1 and 2, resulting in two new 

genomes made up of the same chromosome “primitives” that form the bodies of the parent 

genomes. Then, point mutations are executed on randomly-selected genes within each 

chromosome, which essentially acts to create new types or “species” of combat agents. It should 

be noted that the crossover operator treats individual chromosome vectors as indivisible so that 

chromosomes within a parent genome remain intact after crossover. Changes within a 

chromosome can thus only occur via point mutation, which acts to ensure perpetuated diversity 

of battlefield capabilities among the agents making up a combat unit (Ilachinski 1996). 

3.1.5 Genome Cost Filtering 

The final process still to be addressed within the Blue/Red coevolutionary cycle described in 

section 3.1.1 is cost-constrained evolution, where each “hardware-based” gene within all agent 

chromosomes in a genome (i.e., chromosome genes G0 through G18 inclusive) is allowed to 

mutate within a preset cost-constrained interval. In order to incorporate cost-constrained 

evolution into the GA, the cost-filtering routine was introduced into the coevolution software 

framework (see Figure 11). This routine works in conjunction with both the random_genome

and reproduce routines and a shared GA configuration file to filter each newly-created 

candidate genome based on its compliance with a set of cost constraints. Genomes that pass the 

 * Although both parent genomes must share the same crossover variance, the genome separation point (which must lie within 

the crossover variance range) need not be the same for both parent genomes. 
37

cost filter compliance test are directed into the Blue and Red genome candidate populations as 

appropriate, while failure to comply causes the genome candidate to be rejected. The cost 

constraints making up the cost filter are discussed next. 

The cost filter assumes that the total cost of a genome is simply 

∑ ∑=

=

chromosomes

G

gene G

genome gene

18

cost cost ,

0

 (21) 

where costgene is the implementation cost (in generic economic cost units) of a particular 

hardware gene. The implementation cost function used to calculate costgene is illustrated in 

Figure 16. Here, the implementation cost of a gene is characterized by the following two 

parameters: (1) the installation cost install

gene cost , which is the cost required to initially “install” or 

turn on the hardware gene, and (2) the incremental cost increment

gene cost , which is the cost required 

to increase the gene allele value by one unit. The final gene cost is then 

(k 1) , gene allele

increment

gene

install

gene cost = cost + − ∗cost (22) 

where kallele is the integer value of the gene allele. Then, the set of cost constraints making up the 

genome cost filter includes (a) max

chromosome cost (the maximally allowed total cost of an individual 

chromosome) and (b) max

genome cost (the maximally allowed cost of a genome). Upon generation 

by either the random_genome or reproduce routines, constraint (b) is first applied to a 

genome candidate after its cost is calculated via equations 21 and 22. Given that the genome 

candidate cost < max

genome cost , constraint (a) is next applied. If the genome candidate cost 

< max

chromosome cost , then the candidate is added to the Blue or Red genome pool as appropriate. 

Figure 16. GA cost function for hardware-specific genes within a 

chromosome. 
38

3.1.6 Information Operations Simulations 

In the following subsections, the combat scenario, GA configuration conditions, and simulation 

outputs for IO simulations utilizing the coevolutionary software framework and constituent 

software elements described in sections 3.1.1–3.1.5 are presented and discussed. 

3.1.6.1 Combat Scenario 

The combat scenario depicted in Figures 17 and 18 was used in conjunction with the 

coevolutionary software framework depicted in Figure 11 to run GA benchmark tests. Figure 17 

illustrates the flat virtual battlefield terrain (generated by running the terrain routine with the 

“empty” option selected) occupied by Blue and Red combat agents, which begin a simulation run 

in the southwest and northeast quadrants of the terrain, respectively. Combat agents are initially 

positioned at the start of a simulation instance in randomly selected cells within adjustable 

subquadrants of the CA universe by running the place routine with the “corners” option 

selected. 

Figure 17. Basic Blue/Red combat scenario set on a flat plane. 

Figure 18 illustrates the territorial “importance” gradient that is superimposed over terrain and is 

used to motivate combat agent movement towards a goal (as previously discussed in section 

2.3.10.2). Within this gradient, blue-colored terrain is “neutral” and thus minimally important, 

while red-colored terrain represents operationally “critical” territory. Thus, combat agent 

territory unfolds from the “no-man’s-land” along the center diagonal towards critical Blue- and 

Red-controlled objective points in the southwest and northeast corners of the 2-D battlespace, 

respectively. Based on the allele values assigned to genes G25 through G28 within a combat agent 

chromosome, an agent will be driven to either advance or retreat along successive importance 

gradient contours. 
39

Figure 18. Territorial importance gradient associated with the basic Blue/Red combat 

scenario. 

3.1.6.2 Coevolutionary Simulation Configuration 

Once the combat scenario has been properly delineated, the GA, survival fitness function, and 

Blue/Red genome configuration files are defined. These configuration files are presented in 

Tables 1–3, respectively. The parameters listed in Table 1 are used by the GA to control the 

coevolutionary dynamics of both the Blue and Red candidate genome populations. The second 

parameter (maximal number of allowed successive generations) provides an automated 

termination point for a coevolutionary simulation; the user can, however, manually terminate a 

simulation prior to this point if so desired. 

Next, the parameters listed in Table 2 are used to set the duration of a Blue/Red combat instance 

and to construct the respective survival fitness objective functions for both Blue and Red combat 

units. Here, Blue’s primary mission objective is to prevent any level of surviving enemy 

penetration into friendly territory (characterized by the fitness function F7

[tn]) with a secondary 

objective to invade the enemy’s territory no matter what the friendly attrition cost (characterized 

by the fitness function F8

[tn]), while the reverse is true for Red. These survival fitness objective 

functions force both combat units to search for capability configurations and tactical behaviors 

that serve to achieve a multiobjective goal. 

Finally, the parameters listed in Table 3 define the size, cost, and gene allele value constraints 

that are imposed upon both Blue and Red genome candidates throughout the entirety of a 

multigenerational coevolutionary simulation run. The first constraint was chosen to imply that 

Blue was logistically constrained to maintaining a small unit, while Red was not. The net 

genome cost constraint was chosen to imply that Blue, although logistically constrained, had 
40

Table 1. The GA configuration file used in the coevolutionary simulation. 

Parameter Description Value 

Size of the CA lattice. 136 cells × 136 cells 

Maximal number of successive generations allowed 

within a coevolutionary simulation. 

2400 generations 

The size of both the Blue and Red candidate genome 

pools that should be maintained throughout a 

coevolutionary simulation. 

30 genomes 

The number of fittest genomes to use as parents for 

genetic breeding of new genome candidates. 

5 genomes 

The number of simulation instances to run for each 

Blue/Red genome combat pairing. 

3 instances 

The crossover variance for both Blue and Red parent 

genomes used in genetic breeding. 

0.30 

The probability that a particular chromosome within a 

genome is selected for gene mutation. 

0.20 

The probability that a particular gene within a 

chromosome will undergo mutation given that the 

chromosome has been selected for this operation. 

0.01 

Table 2. The survival fitness configuration file used in the coevolutionary simulation. 

Parameter Description Value 

Total simulation run time. 300 time cycles 

Fsurv(tn) fitness function weights. Blue genome Red genome 

F1(tn): prevent enemy penetration into 

 friendly territory. 

0 0 

F2(tn): penetrate into enemy territory. 0 0 

F3(tn): friendly combat unit survival. 0 0 

F4(tn): enemy combat unit casualties. 0 0 

F5(tn): sense the battlefield. 0 0 

F6(tn): jam enemy communications. 0 0 

F7(tn): prevent enemy penetration into 

 friendly territory with dynamic renormalization. 

0.7 0.3 

F8(tn): penetrate into enemy territory with dynamic 

renormalization. 

0.3 0.7 

F9(tn): collective friendly situational awareness of 

friends. 

0 0 

F10(tn): collective friendly situational awareness of the 

enemy. 

0 0 

greater economic resources at its disposal than did Red. The allele value constraints inform the 

GA of maximal-allowed values of specific mutable genes within a chromosome (i.e., speed, 

sensor range, fire range, fire strength, armor strength, carried fire rounds, carried land mines, and 

carried RF jammer bombs), as well as the intentionally fixed allele values assigned to seven 

immutable genes (i.e., sensor detection probability, sensor false alarm probability, carried UGS
41

Table 3. The genome configuration file used in the coevolutionary simulation. 

Parameter Description Blue Genome Red Genome 

Maximum allowable number of chromosomes within 

a genome. 

25 1024 

Maximum allowable net genome cost. 800,000 cost units 500,000 cost units 

Maximum allowable combat agent fire range (G0). 10 cells 10 cells 

Maximum allowable combat agent fire strength (G2). 20 20 

Maximum allowable number of carried firepower 

rounds (G3). 

255 255 

Maximum allowable combat agent armor strength 

(G4). 

20 20 

Maximum allowable combat agent sensor range (G5). 40 cells 20 cells 

Maximum allowable number of carried land mines 

(G11). 

255 255 

Maximum allowable number of carried RF jammer 

bombs (G12). 

255 255 

Maximum allowable combat agent speed (G17). 10 10 

Fixed sensor probability of target detection (G6). 0.95 0.95 

Fixed sensor probability of false alarm (G7). 0.05 0.05 

Fixed combat agent communication range (G8). 40 cells 20 cells 

Fixed combat agent RF jamming range (G9). 5 cells 5 cells 

Fixed number of carried UGS units (G10). 0 0 

Fixed UGS unit deployment range (G13). 0 0 

Fixed probability of retaliating against an enemy 

(G19). 

1.00 1.00 

units, UGS deployment range, communication and RF jamming ranges, and retaliation 

probability).*

 Chromosome genes that are not specifically addressed in this figure are thus 

allowed to mutate without allele constraints (except for preconstrained genes such as 

probabilities and behavioral weights). The allele value constraints were chosen to imply that 

Blue also had access to superior sensor and communication hardware than did Red. 

3.1.6.3 Simulation Results 

A coevolutionary Blue-vs.-Red simulation (using the scenario and GA/fitness/genome 

configurations described in sections 3.1.6.1 and 3.1.6.2, respectively) was run on a 32-CPU SGI 

Origin 3800 computer. Here, Blue/Red combat instances for each evolutionary generation of 

candidate genomes were run in parallel, after which calculated survival fitness values were 

centrally averaged and reported. Three combat simulation instances were executed (and then 

averaged) for each Blue/Red genome candidate pairing with 30 pairings per generation. The 

latter situation was iteratively realized by randomly selecting one candidate genome from both 

Blue and Red genome pools for a combat pairing and then removing the selected genomes from 

their respective pools and repeating the random selection process until all genomes have been 

 *

 These seven genes were predefined as immutable, implying a scenario where sensor, communication, and RF jamming 

hardware capabilities were constrained by currently available commercial off-the-shelf (COTS) technology (which is not 

assumed to include UGS technology) and an agent will always return fire when engaged. 
42

paired for combat. Each combat simulation was run for 300 time cycles (see Table 2), at which 

point values of the time-averaged survival fitness 〈Fsurv〉 for both Blue and Red genomes were 

evaluated and reported. Then, for both Blue and Red units, the top five candidate genome 

scorers from each generation (see Table 1) were used to produce the next generation of 30 

candidate genomes via random mating pairs. 

Figure 19 depicts the coevolutionary dynamics of Blue/Red average and maximal time-averaged 

survival fitness levels (y-axis) measured across 1360 generations (x-axis), where Blue and Red 

values of 〈Fsurv〉 are based on the fitness function weights presented in Table 2. Here, Red 

quickly “discovers” unit configurations that serve to maintain its average fitness within the 

interval [0.38, 0.43] which, in turn, motivates Blue to compensate with new configurations that 

likewise drive average Blue fitness to the interval [0.49, 0.59]. This coevolutionary process 

nicely illustrates the “Red Queen Principle” from evolutionary biology, where for an 

evolutionary system, continuing development is needed just in order to maintain its fitness level 

relative to the systems it is coevolving with (Van Valen 1973). Since, due to computational 

runtime constraints, the coevolutionary process was prematurely terminated before any strong 

evidence of survival fitness convergence was evidenced, it remains unclear whether the Blue and 

Red combat units would ever break out of the “Red Queen” oscillatory pattern illustrated in the 

figure. If the current theory of coevolutionary neutral mutations (where gene mutations produce 

insignificant variations in relative fitness [Bar-Yam 1997; Whitfield 2002]) is applicable within 

the current context, then fitness convergence might never be achievable without relaxing some of 

the gene allele and cost constraints defined in Table 3. 

Further evidence of continuing development of both Blue and Red combat units throughout the 

coevolutionary process displayed in Figure 19 is provided by a classification analysis of Blue 

and Red genome populations. Table 4 defines a set of combat agent types or “classes” specified 

by agent sensor and firepower ranges, while Figure 20 illustrates the correlated coevolutionary 

dynamics of optimal Blue and Red unit configurations relative to this set of classes. Sensor 

classes include zero-, short-, medium-, and long-range sensor capability, while firepower classes 

are restricted to zero-, very short-, and short-range fire. The plots in Figure 20 measure, for the 

maximal-fitness genome in both populations, the number of Blue/Red combat agents within a 

particular sensor/firepower class (y-axis) across the 1360 generations making up the 

coevolutionary process (x-axis). * Here, Blue unit configuration is observed to oscillate between 

variable mixtures of medium- and long-range sensor agents with variable-range firepower 

weapon capability, finally settling into a mode where most agents have midrange sensor and 

short-range firepower capability. The Red unit, on the other hand, which must execute the more 

operationally challenging primary role of invader, coherently explores sensor/firepower classes 

 * A curve-smoothing algorithm has been applied to these plots (that averages class population levels over 10 successive 

generations) to make them easier to read. 
43

Figure 19. Coevolutionary dynamics of Blue/Red time-averaged survival fitness levels (y-axis) 

measured across 1360 coevolutionary generations (x-axis). 

Table 4. Definition of a set of combat agent classes based on sensor/firepower range combinations. 

Combat Agent Class Gene G5

(Sensor Range) 

Gene G0 

(Firepower Range) 

No sensor/no firepower 0 0 

Short-range sensor/no firepower 1–10 cells 0 

Medium-range sensor/no firepower 11–25 cells 0 

Long-range sensor/no firepower 26–40 cells 0 

No sensor/very short-range firepower 0 1–5 cells 

Short-range sensor/very short-range firepower 1–10 cells 1–5 cells 

Medium-range sensor/very short-range firepower 11–25 cells 1–5 cells 

Long-range sensor/very short range firepower 26–40 cells 1–5 cells 

No sensor/short-range firepower 0 6–10 cells 

Short-range sensor/short-range firepower 1–10 cells 6–10 cells 

Medium-range sensor/short-range firepower 11–25 cells 6–10 cells 

Long-range sensor/short-range firepower 26–40 cells 6–10 cells 

until finally settling on a medium-range sensor and short-range fire for most agents. It should be 

noted that total unit populations are variable across successive generations due to genome size 

and cost filtering. 

Next, Table 5 defines a second set of combat agent classes specified by agent behavioral 

weighting combinations, while Figure 21 illustrates the correlated coevolutionary dynamics of 

optimal Blue and Red unit configurations relative to this new set of classes. Behavioral classes 
44

(a) 

(b) 

Figure 20. Coevolutionary dynamics of optimal Blue and Red unit configurations relative 

to the set of combat agent sensor/firepower range classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
45

Table 5. Definition of a set of combat agent classes based on behavioral gene weighting combinations. 

Combat Agent Class 

Gene G20 (Prob. of 

Initiating Attack) 

Gene G23 (Attraction 

Towards Enemy CM) 

Gene G24 (Repulsion 

From Enemy CM) 

Passive/noncovert < 0.50 > 0.50 < 0.50 

Passive/covert < 0.50 < 0.50 > 0.50 

Passive/indecisive < 0.50 > 0.50 > 0.50 

Passive/unconcerned < 0.50 < 0.50 < 0.50 

Aggressive/noncovert > 0.50 > 0.50 < 0.50 

Aggressive/covert > 0.50 < 0.50 > 0.50 

Aggressive/indecisive > 0.50 > 0.50 > 0.50 

Aggressive/unconcerned > 0.50 < 0.50 < 0.50 

are formed by combining binary gene values (i.e., allele levels that are either < 0.50 or > 0.50) of 

agent attack initiation probability and attraction towards/repulsion from enemy unit CM. Similar 

to Figure 20, the plots in Figure 21 measure (again for the maximal-fitness genome in both 

populations) the number of Blue/Red combat agents within a particular behavioral class across 

the coevolutionary process. In this case, Blue seems to haphazardly explore various classes, then 

settling on a passive/indecisive behavioral mode for over 600 generations, and finally switching 

over to a mixture of passive/indecisive and aggressive/indecisive agents at the end of the 

coevolutionary run. This is not surprising, in that Blue’s primary mission is defensive and thus 

Blue agents can afford to be “indecisive” (effectively immobile) and less “aggressive” than Red 

agents. After an initial exploratory period, Red, on the other hand, first “discovers” a behavioral 

configuration where all agents act in an aggressive (firing a weapon without provocation) and 

noncovert (moving directly towards Blue agents) manner that serves to maintain its operational 

fitness. Then, when Blue counters with behaviors that stress Red fitness, Red, in turn, 

progressively adopts passive and noncovert, aggressive and noncovert, aggressive but indecisive 

(trying to move both towards/away from Blue), and aggressive but covert modes of behavior, 

finally settling on the last mode (with a small fraction of the Red unit continuing to explore other 

behaviors). Again, Red faces the greater evolutionary pressure since its primary mission is 

invasive in nature and thus benefits by coherently moving towards better strategic behavioral 

modes. 

The preceding classification examples were presented to illustrate two limited perspectives on 

Blue/Red coevolutionary dynamics by focusing on specific genes within the combat agent 

chromosome. A true picture of the coevolutionary dynamics can only emerge by portraying the 

20-dimensional response hypersurface generated by class population levels based on the 19 

mutable genes within the agent chromosome (with genome generation providing the last degree 

of freedom).* While this is, of course, impossible to illustrate, it is feasible to provide 

2-D “slices” of the response hypersurface depicting class population levels (as a function

 *

 This also assumes that the behavioral attraction weights contained in genes G21, G23, G25, and G27 can be combined with the 

repulsion weights contained in genes G22, G24, G26, and G28, respectively, thus reducing the number of dimensions required to 

track mutable genes from 23 down to 19. 
46

(a) 

(b) 

Figure 21. Coevolutionary dynamics of optimal Blue and Red unit configurations relative 

to a set of combat agent behavioral classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
47

of gene-specific classes) across the multigenerational coevolutionary process. Plots of these 

slices are included in the Appendix. 

Careful examination of these gene classification plots reveals, as was initially suggested by the 

plots in Figures 20 and 21, that Red’s hardware capability evolution was generally more directed 

than Blue’s. At the end of the 1360-generation coevolutionary simulation, both Blue and Red 

units end up with the maximal-permitted fire range (i.e., “short range” firepower), “high” fire 

strength, two crew members,*

 “fast” maximum speed, and air-travel capability.†

However, Blue firepower round supply moves up and then down, while Red supply steadily 

increases. Both Blue and Red armor strengths start “high” and then decrease (with Red starting 

to rise again at the end of the coevolutionary simulation). Blue sensor range continuously jumps 

up and down, while Red settles on a “moderate” sensor range and stays there. The values of 

Blue’s land mine supply and mine/jammer bomb Pdeploy also jump up and down, while Red again 

settles on a “moderate” level for both these genes well before the end of the simulation. 

There are other specific asymmetric capability levels between Blue and Red that probably play a 

key role in facilitating Blue’s superior survival fitness levels. Although Blue (principally the 

defender) ends up with a “low” firepower Phit level while Red (principally the invader) finishes 

with a “high” value of that gene, Blue quickly settles on and stays with a “very large” jammer 

bomb supply while the poorer Red is forced to stay with a “moderate” supply level. This allows 

the defensive Blue unit greater capability to disrupt acquired target data sharing amongst agents 

in the invasive Red unit. Also, Blue’s signature strength is maintained at a “low” level, while 

Red signature strength is forced to stay at a “high” level (due to lower levels of available 

financing). This asymmetry allows the Blue unit to generally avoid Red acquisition and fire 

(even though Red’s capability to deliver a target hit is greater), while the Red unit’s acquisition 

and subsequent engagement by Blue forces is relatively guaranteed by comparison. 

The evolution of the behavioral modes within the Blue and Red units is a more complicated 

issue. Blue adopts and stays with a “moderate” likelihood of initiating an attack, while Red 

gradually settles on a “high” initiate attack probability; this makes sense given their respective 

defender and invader roles. However, although both Blue and Red end, up acting “indecisive” 

about moving towards/away from friendly agent clusters, Red quickly adopts a tactic to covertly 

avoid enemy agent clusters (while Blue remains indecisive regarding this tactic). While neither 

the Blue nor Red units are prone to stay on their own territory, in the end, Blue wants to invade 

Red’s territory (even though the former’s primary mission is defensive) while Red is again 

“indecisive” regarding enemy territory invasion (thus, only penetrating the outer reaches of Blue 

territory). Finally, both Blue and Red perpetuate a “low” probability of randomly changing 

 *

 There is a mutational bias in the GA so that an agent will essentially never have only one crew member (this was 

programmed to conform to the convention of functional redundancy in the human crew of a military platform). 

† This mode of travel is likely a code artifact, since the flat terrain used in this simulation provides no operational advantage 

of using air flight over ground transport. 
48

speed and direction, suggesting that occasional unpredictable movement on the part of an agent 

can help to maintain the survival fitness of its unit within a volatile environment. It is possible 

that the Red unit’s generally inferior hardware capabilities prevent it from settling upon a 

straightforward set of behavioral tactics (i.e., the simultaneous attraction/repulsion behavior 

characteristic of indecision doesn’t tend to facilitate achievement of a strategic goal). It is also 

very likely that the static nature of agent behavioral modes during a simulation instance doesn’t 

provide the adaptive capability necessary for a unit to strategically exploit situations that evolve 

during the simulated combat. 

It is interesting to try to characterize the degree of speciation within these results, where 

speciation refers (in the current context) to the process where agents within a combat unit evolve 

differentiated hardware capabilities and behavioral modes within the same evolutionary 

generation. The degree of agent speciation can be qualitatively assessed over the coevolutionary 

multigenerational simulation run via visual inspection of the classification plots in the Appendix. 

The difference in speciation between the Blue and Red combat units is immediately obvious. 

While neither Blue nor Red units consistently engage in the speciation process, Blue is 

considerably more active in this regard than Red. In fact, the Red unit appears to coherently 

avoid speciation across all chromosome genes except when adopting a new gene class (in which 

case, transient speciation occurs only as all agents within the unit progressively switch from one 

class to another). This is likely due to the mechanics of the GA (i.e., crossover within an agent 

chromosome is avoided) in combination with Red’s more strategically complex role as the 

invader (where agent speciation must be combined with dynamic C2 in order to be operationally 

effective). 

In the next section, the embedding of a quasiscripted combat agent C2 process directly into the 

CA simulation engine (and the subsequent IO simulation results) is explored in detail. 

3.2 Enhanced Network-Centric Quasiscripted Simulation 

The second and enhanced version of the CA simulation engine, “combat_proto” (so named 

for its utility in prototyping more complex combat agent behaviors) models the deployment and 

function of notional internetted UGS and multihop message relaying, and thus extends the 

network-centric capabilities of combat_ga. However, since combat_proto (also written in 

the Cellang programming language) employs a semiscripted time-dependent C2 process directly 

built into the associated code, this CA simulation engine cannot be used in conjunction with the 

GA (which assumes combat agents to maintain stationary behaviors throughout a combat 

simulation instance). 

3.2.1 Simulation Framework 

Since the simulation of UGS networks and multihop message relaying necessitates the extensive 

addition of new time steps into the CA update time cycle, combat_proto is ill-suited for use 

within a multigenerational coevolutionary simulation. In addition, the introduction of a 
49

quasiscripted C2 process directly into the CA code overrides the static behavioral modes that 

characterize Blue/Red combat in the previously-discussed simulation framework. Thus, in the 

framework discussed in this section, the GA has been removed, and the dynamic genome pools 

are replaced by static Blue and Red combat unit genomes. 

In the new framework depicted in Figure 22, hand-scripted configuration genomes are 

constructed in order to represent a Blue unit of networked mobile platforms making up a 

network-centric combat unit as well as a Red unit of loosely organized, nonnetworked platforms. 

Combat simulations are run using the combat_proto CA engine in combination with the 

terrain, territory, and place routines to respectively generate a simulation landscape 

and a territorial importance gradient superimposed over the landscape and to initially position 

Blue and Red combat agents. Then, the data filter dynamically calculates preselected fitness 

functions (the entire set of which was described in section 3.1.3) of each opposing combat agent 

unit within the simulation, which can include hostile IO actions. Finally, the fitness function 

outputs of the filter are analyzed in a time series format (as opposed to weighting the functions 

and then combining them into the survival fitness objective function as was done in the previous 

framework). 

Figure 22. Software framework for running the enhanced Blue/Red quasiscripted combat 

simulation. 

3.2.2 The combat_proto CA Simulation Engine 

As mentioned in the preceding section, the combat_proto CA simulation engine (also written 

in Cellang [Eckart 1992a, 1992b]) extends the combat agent network-centric capabilities 

encoded into the combat_ga simulation engine by also modeling the deployment and function 

of notional UGS networks and multihop message relaying between allied agents. As with 

combat_ga, the dynamics of combat_proto involve a cyclic sequence of events that is 

executed during each iterated update of a combat simulation. This sequence is depicted in Figure 

23, which illustrates the following cycle of processes (where a process can unfold over one or 

more sequential simulation time steps):
50

Figure 23. Cyclic dynamics of the combat_proto CA simulation engine. 

• Combat agents move into an adjacent cell within their local von Neumann neighborhood 

based on their goal-driven movement decisions (see section 2.3.10.2). This process is 

executed over one time step. 

• Combat agent and internetted Blue UGS unit communication reception are jammed based 

on proximity to previously deployed RF jammer bombs with residual battery power. In 

this version of the CA engine, (a) combat agents are not equipped with on-board jamming 

capability (i.e., gene G9 within all agent chromosomes is set equal to 0), and (b) jammer 

bomb communication disruption is restricted to conventional nine-cell Moore 

neighborhoods (Figure 24). Next, Blue UGS units sense their local environment (i.e., a 

single lattice cell). Both of these events occur within a single time step. 

• Blue UGS units broadcast their acquired local situational data to other units within the 

UGS network (based on the UGS dynamics discussed in section 2.3.6.1). This process 

unfolds over 199 sequential time steps (at the conclusion of which all connected portions of 

the UGS network should share the same SA data). 
51

Figure 24. Reduced RF jammer bomb 

neighborhood consisting of 

the center bomb cell plus 

eight neighboring cells. 

• Sensor-equipped combat agents acquire SA data transmitted from sensed battlefield 

entities. In addition, Blue sensor-equipped agents can also acquire SA data from Blue UGS 

network access nodes.* This data transmission process utilizes artificial “square waves” 

(which are easy to implement within the regular CA lattice) that travel at a speed of one 

cell per time step and can be blocked by terrain obstacles such as mountains or buildings. 

Thus, target sensing by combat agents is assumed to function in a “line-of-sight” mode. 

This process unfolds over 149 sequential time steps (at the conclusion of which all sensorequipped agents with uninterrupted line-of-sight pathways to sensed entities should have 

acquired their data). 

• Blue combat agents share acquired sensor data via their allied communication network 

(while Red agents do not intercommunicate), where an agent will rebroadcast each new 

data report it receives over the network to ensure that all Blue agents share a coherent 

battlefield situational awareness. The communication network also transmits data via 

square waves that travel at a speed of one cell per time step; in this case, however, 

transmission is not limited to line-of-sight interaction (and, thus, terrain obstacles have no 

effect on message propagation). This process unfolds over 199 sequential time steps (at the 

conclusion of which all nonjammed Blue agents should share the same SA data). 

• Combat agents fire upon acquired targets (where multiple agents may engage the same 

target). Fired rounds travel at a propagation speed of one cell per time step until the target 

 *

 The use of specific access nodes within the UGS network was notionally engineered to avoid the cumbersome message 

saturation that would result if sensor-equipped Blue agents were allowed to acquire stored SA data from all of the nodes within

the network. 

Jammer

Bomb
52

cell is reached and are also not restricted by line-of-sight between engager and target. This 

process unfolds over 150 sequential time steps (at the conclusion of which all engaged 

target agents should have received fire). 

• Engaged targets react to received fire (i.e., either die or survive engagement with reduced 

armor capability). This last process is executed over one time step. 

Thus, one entire cycle of simulation processes unfolds over a total of 700 sequential time steps, 

which are iteratively repeated throughout a combat simulation instance. 

3.2.3 “Fombler’s Ford”-Inspired Scenario 

Figure 25 depicts the virtual battlefield terrain (inspired by the Defense Advanced Research 

Projects Agency’s notional “Fombler’s Ford” scenario [Gorman 2000] created to illustrate 

notional Future Combat Systems operations) within which Blue agents are positioned in the 

center defending their territory from Red invaders advancing inward from the outer edge of the 

CA lattice. Associated with this terrain is the territorial “importance” depicted in Figure 26. In 

this scenario, geographical terrain features such as hills (medium gray), buildings (dark gray 

regular lattice of cells), a mountain (black), and a river (aqua blue) are added to impact combat 

agent mobility and UGS placement. As was the case with the importance gradient previously 

introduced in section 3.1.6.1, the Fombler’s Ford gradient shown in Figure 26 is superimposed 

over the associated terrain and is used to motivate combat agent movement towards a goal 

(where blue-colored terrain is “neutral,” and red-colored terrain represents operationally 

“critical” territory). Here, combat agent territory unfolds from the “no-man’s-land” along the 

periphery of the CA lattice to a critical Blue-controlled objective point at the center of the 2-D 

battlespace towards which Red agents advance. Finally, Red-controlled territory is not used in 

this scenario (and, thus, the Red unit can be thought to represent a collection of loosely organized 

guerillas or terrorists). 

The hand-scripted combat agent chromosomes making up the respective Blue and Red combat 

unit genomes are shown in Figures 27–32. In this scenario, the Blue unit is composed of the 

following types of combat agents: 

• Three “command and control vehicle” (C2V) agents (Figure 27) equipped with short-range 

sensor, short-range firepower, and long-range interagent communication capabilities, as 

well as a nonzero value of the human crew gene G16. Since the only autonomous C2 

capability built into this type of agent is to allow other Blue agents without human crew to 

engage acquired targets, the C2V agents are functionally redundant (i.e., they do not 

control specific teams within the Blue unit). Thus, “unmanned” Blue firepower-equipped 

agents can engage targets provided (1) at least one C2V agent survives and (2) the 

engaging agent’s communication reception remains unjammed. Finally, these agents, 
53

Figure 25. Notional Fombler’s Ford terrain imposed onto the CA lattice. 

Figure 26. Territorial importance gradient associated with the Fombler’s Ford-inspired 

combat scenario. 

although designated as “ground vehicles,” are not given mobility capability in this scenario 

(since they were specifically designed to maintain a static defensive posture throughout the 

duration of a combat simulation instance). 
54

Figure 27. Blue C2V agent chromosome. 

Figure 28. Blue short-range direct-fire (DF) agent chromosome. 
55

Figure 29. Blue long-range sensor agent chromosome. 

Figure 30. Blue “rockets-in-a-box” agent chromosome. 
56

Figure 31. Red “pickup truck” agent chromosome. 

Figure 32. Red medium-range DF agent chromosome. 
57

• Three C2V agents (Figure 27) equipped with short-range sensor, short-range firepower, and 

long-range interagent communication capabilities, as well as a nonzero value of the human 

crew gene G16. Since the only autonomous C2 capability built into this type of agent is to 

allow other Blue agents without human crew to engage acquired targets, the C2V agents 

are functionally redundant (i.e., they do not control specific teams within the Blue unit). 

Thus, “unmanned” Blue firepower-equipped agents can engage targets provided (1) at least 

one C2V agent survives and (2) the engaging agent’s communication reception remains 

unjammed. Finally, these agents, although designated as “ground vehicles,” are not given 

mobility capability in this scenario (since they were specifically designed to maintain a 

static defensive posture throughout the duration of a combat simulation instance). 

• Two DF agents (Figure 28) equipped with the same capabilities assigned to the C2V agent 

except for the lack of a human crew (i.e., this DF agent type represents an unmanned 

ground vehicle). This type of agent also maintains a static defensive posture throughout a 

simulation run and thus serves as a “sacrificial diversion” that draws enemy fire away from 

more important targets. 

• Three unmanned sensor agents (Figure 29) equipped only with long-range sensor and 

communication capabilities. These agents also maintain static defensive postures 

throughout a simulation run. 

• Four rockets-in-a-box agents (Figure 30) equipped with short-range sensor, long-range 

firepower, long-range communication, and mobility capabilities, as well as a plentiful 

supply of 270 UGS missiles (with nine deployable UGS units packed into a missile). 

These agents are provided with mobility to facilitate their deployment of the Blue UGS 

network (see the next paragraph), where agent movement is motivated by attraction to a 

combination of Blue agent CM and Blue territory (to move into their defensive postures 

after UGS network deployment). Finally, gene G20 (i.e., probability of initiating an attack) 

is set equal to 0.20 for all of these agents to imply an inherent difficulty in distinguishing 

viable Red targets apart from indigenous “neutral” agents (which are assumed to be present 

but are not explicitly modeled in the scenario). 

The total Blue force thus consists of 12 different combat agents drawn from 4 different agent 

classes or “species.” 

On the other hand, the Red unit is composed of the following types of combat agents: 

• Twenty pickup truck agents (Figure 31) equipped with very short-range sensor (i.e., human 

vision provided by the “driver” and “passenger” riding in the truck bed), very short-range 

firepower (i.e., a machine gun belonging to the truck’s “passenger”), and mobility 

capabilities, as well as a nonzero value of the human crew gene G16 and a supply of RF 

jammer bombs (that will be deployed from the truck bed by the “passenger”). These agents 
58

do not intercommunicate or communicate with other Red agents and thus function in an 

operationally autonomous fashion. Finally, agent movement is motivated only by an 

attraction to Blue territory. 

• Ten DF agents (Figure 32) equipped with medium-range sensor, medium-range firepower, 

and mobility capabilities. These agents also do not intercommunicate or communicate with 

Red pickup truck agents and thus are operationally autonomous. Unlike the Blue rocketsin-a-box agents, these DF agents have no difficulty in positively identifying enemy agents 

once acquired. Finally, agent movement is motivated only by an attraction to Blue 

territory. 

The total Red force thus consists of 30 different combat agents drawn from two different agent 

species. 

As was previously mentioned, a C2 process was directly built into the combat_proto CA 

simulation engine that guides combat agent activities via a semiscripted time-dependent schedule 

called a synchronization matrix (Figure 33). In this C2 process, each specific type or class of 

Blue and Red combat agent is identified, along with a schedule of combat “orders” assigned to 

that agent type as a function of mission time (represented by the time axis below the matrix). 

The C2 activity commences some 830 time steps prior to mission commencement. Here, Blue 

rockets-in-a-box agents equipped with “UGS missiles” fire UGS units into place within the 

neutral terrain surrounding Blue territory, while other Blue agents maintain a stationary 

defensive posture. This UGS network deployment process is depicted in Figure 34 as a sequence 

of simulation window snapshots. Each of the four Blue rockets-in-a-box agents is initially 

positioned in the center of a battlefield quadrant, from which vantage point they launch the UGS 

missiles. Upon landing at a target cell, a UGS missile acts to populate a 3 × 3 cell sublattice with 

internetted UGS units following an average 75% packing density (i.e., the probability that a cell 

within the nine-cell UGS sublattice receives a UGS unit is equal to 0.75).* Once the UGS 

network is in place, the rockets-in-a-box agents move to defensive postures and remain there. 

Then, at time t = 0, a “first wave” of the Red seemingly-neutral pickup truck agents (many of 

which are acquired and fired upon/destroyed by the network-centric Blue unit) drop jammer 

bombs as they penetrate into the peripheral Blue UGS network. Finally, at time step t = 4200 

(i.e., the commencement of the seventh simulation time cycle), a “second wave” of the Red 

agents equipped with medium-range firepower capability advances toward Blue in the 

undetected “shadow zones” within the UGS network created by the dispersed jammer bombs. 

The simulation is then allowed to run until a steady-state end condition is achieved (i.e., either 

Blue successfully kills all Red invaders or all surviving Red agents reach the center of the 

Fombler’s Ford terrain). 

 * The UGS packing density is a variable control parameter that will be utilized in a sensitivity analysis presented in a 

subsequent section of the report. 
59

Figure 33. Synchronization matrix for the Fombler’s Ford-inspired combat scenario. 

Starting at t = 0 and then moving forward in time, a combat simulation is instantiated by running 

the combat_proto CA engine in combination with the Fombler’s Ford terrain, associated 

territorial importance gradient, and the synchronization matrix. Figure 35 depicts the resultant 

events or phases that unfold throughout one simulation update cycle of a CA simulation run. In 

Figure 35a, Red combat agents enter the peripheral neutral territory surrounding Blue territory 

and are sensed by the notional UGS network (blue cells). Once a UGS unit senses a combat 

agent that has entered its cell, it transmits agent identity (Red) north/south/east/west neighboring 

UGS units (represented by the flowing green “message fronts”); this allows for situational 

awareness reports to propagate throughout connected portions of the extensive UGS “web.” In 

Figure 35b, sensor messages are sent out by UGS “access nodes” and Blue agents; these 

messages are then “sensed” and received by sensor-equipped combat agents. In the next 

snapshot (Figure 35c), sensor data are shared among Blue agents via their communication 

network. Sensor and communication messages travel as artificial “square waves” to ensure 

symmetric signal propagation throughout the CA lattice. Finally, as shown in Figure 35d, a Blue 

agent fires upon an acquired Red target (where the black line illustrates payload trajectory). 

The propagation dynamics of locally sensed SA data throughout the deployed UGS network is 

illustrated in greater detail as a sequence of simulation window snapshots in Figure 36. In this 

simulation instance, Red pickup truck agents enter the outer edges of the UGS network from the 

north, south, east, and west. Then, as time progresses, local UGS data are shared across 

connected network nodes (where the network cell color indicates the total number of SA data 
60

Figure 34. Four successive snapshots illustrating the deployment of a notional UGS network across 

neutral territory within the Fombler’s Ford landscape. 

messages resident within a node at that point in time). A final equilibrium condition is achieved 

within the network when all interconnected UGS nodes possess the same set of SA data 

messages (time snapshot no. 4 in Figure 36). In the current UGS network configuration, there 

are three distinct disconnected subnetworks that emerge as a function of terrain features (i.e., 

Nichevo Mountain and the Ogenchornya River) that naturally subdivide the overall network. 

(4)

(1) (2)

(3)
61

Figure 35. Four phases within a combat_proto simulation cycle using the Fombler’s Ford-inspired 

scenario: (a) Red agents detected within the peripheral UGS network, (b) sensor data 

propagates from sensed entities to sensor-equipped Blue agents, (c) acquired sensor data are 

shared over the Blue communication network, and (d) Blue fires on Red target. 

The previously discussed UGS network packing density, which is directly correlated with 

deployed UGS web interconnectivity, is related to site percolation. A concept borrowed from 

(a) (b) 

(c) (d) 
62

Figure 36. Four successive snapshots illustrating the propagation dynamics of SA data throughout the UGS 

network. 

statistical mechanics, site percolation refers to the uninterrupted flow of a resource (in this case, 

information) through a contiguous medium comprised of nodes within a 2-D regular lattice and 

is known to emerge in certain types of CA with heterogeneous cell transition rules(Wolfram 

1985). Associated with this process is a critical packing density threshold, pc ≅ 0.593, at which 

(1) (2) 

(3) (4) 
63

point the information flow between nodes changes from locally to globally continuous(Ziff 

1992). Thus, the UGS network packing density, which is meant to represent factors such as local 

environment metrology or terrain that might tend to disrupt orderly UGS unit deployment, 

should always be maintained at a level well above pc. 

Finally, in addition to setting up the UGS network across the neutral territory surrounding the 

central Blue territory prior to mission commencement, the Blue unit is also assumed to have 

configured a field of land mines along the outer edges of its territory prior to commencement of a 

simulation run. This preconfigured minefield is depicted in Figure 37. In the “unlikely” event of 

Red agents reaching the periphery of Blue territory, the minefield is intended to prevent further 

penetration of invader agents via immobilization (see section 2.3.6.2). Since the probability of a 

Red agent losing its mobility capability upon entering a mined cell is equal to 0.5 and the 

minefield is three cells in width, the probability of the agent getting through the minefield with 

mobility intact is 0.53

 = 0.125. 

Figure 37. Preconfigured land mine field surrounding Blue territory. 

The principal objective of the Red unit in this combat scenario is to allow their medium-range 

DF agents to infiltrate through the UGS network and reach the periphery of Blue territory 

without being detected (from which vantage point the Red DF agents can engage Blue targets). 

Thus, the intent of Red is to create unsensed “rips” within Blue’s UGS network (through jammer 

bomb deployment) through which the Red medium-range DF agents may move undetected. 

This “sensor web rip” operation by Red is illustrated in Figure 38, which depicts the channels 

within the UGS network wherein communication reception has been jammed during the 12th 

simulation time cycle. Here, the color of the UGS network nodes again indicates the number of 
64

Figure 38. The status of Red’s sensor web rip 

operation by the end of the 12th 

time cycle within a 

combat_proto simulation 

instance. 

SA data messages resident in a node (see Figure 36 for an explanation of the color code). Given 

that jammer bomb battery lifetimes have been preset to a duration of 10 successive time cycles 

subsequent to bomb deployment, the rips in the UGS network are sufficiently perpetuated to 

allow the Red medium-range DF agents ample opportunity to reach the edge of Blue territory. 

3.2.4 Simulation Results 

Combat agent simulations were run using the software framework depicted in Figure 22 in 

combination with the Fombler’s Ford scenario described in section 3.2.3 in order to explore the 

sensitivity of the following model parameters to IO stress: 

• UGS density - the average packing density of the peripheral Blue UGS network. 

• Blue fire point - the outward distance from the perimeter of Blue territory at which point 

Blue combat agents are allowed to fire on acquired Red targets. 

• Blue shots/cycle - the number of shots a firepower-equipped Blue combat agent can fire 

during one simulation time cycle. 

This sensitivity analysis was predicated on the time series response generated by 4 of the 10 

fitness functions (introduced in section 3.1.3) as applied to the following specific combat units: 

• Blue and Red combat unit survival (F3[tn]; equation 13), 

• Blue’s ability to sense the battlefield (F5[tn]; equation 15), 
65

• Blue’s ability to prevent Red penetration into Blue territory with dynamic renormalization 

(F7[tn]; equation 17), and 

• collective Blue situational awareness of Red (F10[tn]; equation 20). 

In this set of simulations, these fitness functions serve as dynamic measures of collective combat 

unit performance. 

Time series results from the first of six different Fombler’s Ford scenario variants are depicted in 

Figures 39 and 40. The purpose of this initial scenario variant is to illustrate the operational 

impact within an IO-stress-free environment (and, thus, Red “pickup truck” agents do not deploy 

jammer bombs in this variant) of Blue deploying a UGS network with a packing density less than 

the critical site percolation density pc (see section 3.2.3). In this scenario, the model parameters 

are set to (a) UGS density = 0.55, (b) Blue fire point = 10 cells, and (c) Blue shots/cycle = 1. 

Multiple simulation instances of the scenario are run using the set of 20 sequential random seeds 

{0, 1, 2, ..., 19},* and then time series results are averaged and plotted. The Blue unit’s average 

fractional survival (Figure 39a) is significantly reduced to a post-combat level of 0.63, while the 

Red unit’s average survival rapidly drops to a near-zero level following the commencement of 

combat (and then soon achieves total attrition by time cycle 41). Blue’s average fractional 

sensor coverage of the virtual 2-D battlespace (Figure 39b) is reduced from ~0.86 to a bit higher 

than 0.67 through the loss of sensor-equipped Blue agents. The Blue unit’s average “defender 

fitness” capability to prevent Red penetration into Blue territory (Figure 40c) also seriously 

underperforms as a result of the subcritical UGS network packing density, where Red mediumrange DF agents are able to penetrate the periphery of Blue territory (starting at time cycle 20) 

and significantly engage Blue targets until the Red agents are killed off (by time cycle 41). 

Finally, the Blue unit’s average situational awareness of Red (Figure 40d) remains below a level 

of 0.70 out until about time cycle 30 (again attributable to the subcritical UGS density), by which 

time enough Red agents reach locations at the edge of Blue territory where engagement of Blue 

targets is possible. 

In the second Fombler’s Ford scenario variant, the UGS network packing density is raised to a 

level well above pc, while other parameters remain as before. Simulation results from this 

scenario (again averaged over 20 simulation instances using the standard set of random seeds) 

are depicted in Figures 41 and 42. Thus, in these runs, the model parameters are now set to (a) 

UGS density = 0.75, (b) Blue fire point = 10 cells, and (c) Blue shots/cycle = 1. Also, in order to 

quantitatively illustrate the dynamic impact of IO stress on both Blue and Red combat unit 

operational performance, two sets of simulations are run using these model parameters. In the 

first set, Red pickup truck agents operate without jammer bombs. In the second set, these Red 

agents are each equipped with a supply of jammer bombs, as delineated in Figure 31. 

 * Since this sequence of 20 random seeds will also be used in subsequent scenario variant simulation runs for consistency 

purposes, it will henceforth be referred to as the “standard set of random seeds.” 
66

 simulation time cycle

(a) 

 simulation time cycle

(b) 

Figure 39. Fombler’s Ford simulation results, with UGS density = 0.55, Blue fire point = 10 cells, 

and 1 Blue shot/cycle: (a) combat unit survival and (b) Blue sensor coverage.
67

 simulation time cycle

(c) 

 

 simulation time cycle

(d) 

Figure 40. Fombler’s Ford simulation results, with UGS density = 0.55, Blue fire point = 10 

cells, and 1 Blue shot/cycle: (c) Blue’s ability to prevent Red penetration into Blue 

territory and (d) Blue situational awareness of Red. 
68

 

 simulation time cycle

(a) 

 

 simulation time cycle

(b) 

Figure 41. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 10 

cells, and 1 Blue shot/cycle: (a) combat unit survival and (b) Blue sensor coverage. 
69

 simulation time cycle

(c) 

 

 simulation time cycle

(d) 

Figure 42. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 10 cells, 

and 1 Blue shot/cycle: (c) Blue’s ability to prevent Red penetration into Blue territory 

and (d) Blue situational awareness of Red. 
70

As one would expect, the operational impact of simply increasing Blue’s UGS packing density 

from 0.55 to 0.75 is considerable. Blue’s average post-combat survival level (Figure 40a) within 

an IO-benign environment increases to 0.80, while Red’s attrition rate is slightly increased. The 

post-combat fractional Blue sensor coverage (Figure 41b) also increases to a bit over 0.80. 

Blue’s average defender fitness level (Figure 42c) only degrades to a level of ~0.985 (indicating 

a reduction in Red’s ability to penetrate Blue territory). Finally, Blue’s situational awareness of 

Red (Figure 42d) is sustained at levels greater than 0.70. 

However, the addition of IO stress (by equipping the Red pickup truck agents with jammer 

bombs) is seen to significantly degrade the performance of all of the time series metrics depicted 

in Figures 41 and 42. Blue’s post-combat survival level is reduced by almost 25% under stress, 

while Red’s survival increases shortly the after the second invasion wave commencement at the 

seventh time cycle. Blue’s sensor coverage is reduced by ~10% under stress. Blue’s defender 

fitness is slightly degraded by the IO stress, with Red agents able to remain within the outer 

regions of Blue territory for an additional 7–8 simulation time cycles over the unstressed 

occupation time duration. Lastly, Blue’s situational awareness of Red is significantly degraded 

by the rips induced within UGS web continuity via the Red jammer bomb deployment, 

essentially returning to levels observed in the previous scenario variant. 

In the third Fombler’s Ford scenario variant, Blue’s fire point is increased so that Red targets are 

now engaged as soon as they are introduced at the edge of the CA lattice (with other parameters 

remaining as before). Simulation results from this scenario (again averaged over the standard set 

of random seeds) are depicted in Figures 43 and 44. In these runs, the model parameters are next 

set to (a) UGS density = 0.75, (b) Blue fire point = 17 cells, and (c) Blue shots/cycle = 1; again, 

two sets of simulations are run (with and without jammer bombs). Here, Blue’s average 

unstressed post-combat survival level (Figure 43a) increases to 0.92, with Red’s attrition rate 

even slightly greater than in the previous scenario; again, the addition of IO stress degrades the 

former performance and improved the latter. The post-combat fractional Blue sensor coverage 

(Figure 43b) increases to a level of ~0.88 without stress and then drops to 0.837 under stress. 

Next, Blue’s average defender fitness (Figure 44c) virtually remains at ~1.00 without IO stress 

and is then degraded down to similar levels as seen in the previous scenario variant upon the 

introduction of stress. Finally, unstressed Blue situational awareness of Red (Figure 44d) is 

sustained at levels now greater than 0.80; again, these levels drop significantly when Red agents 

are allowed to use their jammer bombs. 

In the fourth Fombler’s Ford scenario variant, Blue’s fire point is reduced to its original value 

while that unit’s firepower engagement rate is increased. Simulation results from this scenario 

(averaged over the standard set of random seeds) are depicted in Figures 45 and 46. In these 

runs, the model parameters are next set to (a) UGS density = 0.75, (b) Blue fire point = 10 cells, 

and (c) Blue shots/cycle = 3; again, sets of simulations with and without IO stress are run. In this 

case, Blue’s average performance within both unstressed and IO-stressed environments 

regarding post-combat survival level (Figure 46a), fractional sensor coverage (Figure 45b), 
71

 

 simulation time cycle

(a) 

 

 simulation time cycle

(b) 

Figure 43. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 17 

cells, and 1 Blue shot/cycle: (a) combat unit survival and (b) Blue sensor coverage. 
72

 

 simulation time cycle

(c) 

 

 simulation time cycle

(d) 

Figure 44. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 17 

cells, and 1 Blue shot/cycle: (c) Blue’s ability to prevent Red penetration into Blue 

territory and (d) Blue situational awareness of Red. 
73

 

 simulation time cycle

(a) 

 

 simulation time cycle

(b) 

Figure 45. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point = 10 

cells, and 3 Blue shots/cycle: (a) combat unit survival and (b) Blue sensor 

coverage. 
74

 simulation time cycle

(c) 

 simulation time cycle

(d) 

Figure 46. Fombler’s Ford simulation results, with UGS density = 0.75, Blue fire point 

= 10 cells, and 3 Blue shots/cycle: (c) Blue’s ability to prevent Red penetration 

into Blue territory and (d) Blue situational awareness of Red. 
75

defender fitness (Figure 46c), and Blue situational awareness of Red (Figure 46d) is actually 

inferior to the previous scenario variant (where Blue fire point = 17 cells). Red’s average 

operational performance, on the other hand, is improved within this variant compared to that 

unit’s performance in the previous scenario. Comparison of Blue and Red unit performance 

within this scenario relative to the previous scenario suggests that allowing Blue an earlier 

opportunity to engage invasive Red agents is more operationally significant than increasing the 

former’s firepower engagement rate. 

In the fifth Fombler’s Ford scenario variant, the packing density of Blue’s UGS network is 

further increased to a maximal level (with other parameters reflecting values from the second 

scenario variant). Simulation results from this scenario (averaged over the standard set of 

random seeds) are depicted in Figures 47 and 48. Now, the model parameters are next set to (a) 

UGS density = 1.00, (b) Blue fire point = 10 cells, and (c) Blue shots/cycle = 1; again, sets of 

simulations are run with and without jammer bombs. In this scenario, both Blue and Red 

average post-combat survival (Figure 47a) reflect levels observed in the fourth scenario variant 

simulations within both benign and IO-stressed environments. Blue’s post-combat fractional 

sensor coverage (Figure 47b) in both benign and stressed environments is slightly improved over 

levels seen in the previous scenario variant (as would be expected given the maximal UGS 

network density). Interestingly, Blue’s average defender fitness (Figure 48c), although reflecting 

similar levels as in the previous scenario, is degraded under stress for a much shorter time 

interval than that observed in all previous scenario variants. This is likely attributable to 

improved Blue situational awareness of Red agents crossing over the UGS network (resulting 

from the maximal UGS packing density). This is indeed confirmed in the plot depicting Blue’s 

situational awareness of Red (Figure 48d), where IO-stressed Blue unit awareness is sustained at 

levels comparable to those seen in the third scenario variant (where fewer Red agents have the 

opportunity to cross the UGS network due to Blue’s employment of an “early response” maximal 

fire engagement point). 

In the sixth and last Fombler’s Ford scenario variant, the values for Blue fire point, Blue 

shots/cycle, and UGS packing density used in the third, fourth, and fifth scenario variants, 

respectively, are combined to create an optimal Blue unit configuration (within the limits of the 

sensitivity analysis). Simulation results from this final scenario (averaged over the standard set 

of random seeds) are depicted in Figures 49 and 50. In this particular run, the model parameters 

are set to (a) UGS density = 1.00 (maximum density), (b) Blue fire point = 17 cells (i.e., Red 

agents are engaged as they are introduced at the edge of the CA lattice), and (c) Blue shots/cycle 

= 3 (with Red shots/cycle still equal to 1). Blue’s average post-combat survival level 

(Figure 49a) is, in this case, only reduced by a little over 5% under IO stress (maintaining a new 

peak level of ~0.90), with an associated peak attrition rate of Red agents under these conditions. 

The average post-combat Blue sensor coverage (Figure 49b) is only reduced from ~0.932 to a 

little over 0.916, reflecting enhanced survivability of sensor-equipped Blue agents. Blue’s 

defender fitness (Figure 50c) almost remains unchanged going from a benign to IO-stressed 
76

 

 simulation time cycle

(a) 

 

 simulation time cycle

(b) 

Figure 47. Fombler’s Ford simulation results, with UGS density = 1.00, Blue fire point = 10 cells, and 

1 Blue shot/cycle: (a) combat unit survival and (b) Blue sensor coverage. 
77

 

 simulation time cycle

(c) 

 

 simulation time cycle

(d) 

Figure 48. Fombler’s Ford simulation results, with UGS density = 1.00, Blue fire point = 10 cells, and 

1 Blue shot/cycle: (c) Blue’s ability to prevent Red penetration into Blue territory and (d) 

Blue situational awareness of Red. 
78

 

 simulation time cycle

(a) 

 

 simulation time cycle

(b) 

Figure 49. Fombler’s Ford simulation results, with UGS density = 1.00, Blue fire point = 17 

cells, and 3 Blue shots/cycle: (a) combat unit survival and (b) Blue sensor 

coverage. 
79

 

 simulation time cycle

(c) 

 

 simulation time cycle

(d) 

Figure 50. Fombler’s Ford simulation results, with UGS density = 1.00, Blue fire point = 

17 cells, and 3 Blue shots/cycle: (c) Blue’s ability to prevent Red penetration 

into Blue territory and (d) Blue situational awareness of Red. 
80

environment, while the Red agent sustains penetration into Blue territory reaching a minimal 

level (relative to model parameter constraints). Finally, Blue’s situational awareness of Red 

(Figure 50d) is still degraded by jammer-bomb-induced UGS web discontinuities, but the 

sustained magnitude and duration of Blue’s awareness here achieves peak levels relative to all 

other scenario variants studied under the sensitivity analysis. These plots quantitatively illustrate 

how providing the Blue unit with a combination of enhanced operational capabilities can act 

together to significantly attenuate the perturbative impact of a form of IO stress that results in 

denial of communication network connectivity. 

Further insight into how enhanced Blue unit operational capabilities act to improve Blue’s 

situational awareness of Red can be gained by characterizing the sustainment of the latter metric 

throughout a combat simulation. This is done through the use of a return map, which is (in the 

current context) a 2-D scatter plot of BSAR (i.e., Blue’s situational awareness of Red) evaluated 

at simulation time cycle tn+1 vs. BSAR evaluated at time cycle tn. A return map illustrates the 

dynamics of a one-dimensional time series (i.e., a time series with just one independent variable) 

in a recursive rather than successive format. Figures 51–53 illustrate BSAR return maps for the 

first, second, and sixth Fombler’s Ford scenario variants, respectively (where each return map 

displays a superposition of data collected from the 20 simulation instances run for each variant). 

In Figure 51, the perpetuated BSAR levels (measured in an unstressed benign environment) 

resultant from the Blue UGS network deployed with a (subcritical) average packing density of 

0.55 form a fairly dispersive point cluster with a CM located at 0.489 and 0.493.* This benignenvironment BSAR cluster is seen to move towards the upper right-hand corner of the plot when 

the UGS packing density is increased to 0.75 (the blue square points in Figure 52), with the point 

cluster CM now located at 0.848 and 0.850. However, the introduction of IO stress into this 

scenario variant serves to slide the BSAR cluster back towards the lower left-hand corner (the 

red cross points in Figure 52), where the cluster CM degrades to a location of 0.555 and 0.554. 

Finally, as demonstrated in Figure 53, the implementation of maximal levels of UGS packing 

density, Blue fire point, and Blue shots per time cycle drives the benign-environment BSAR 

cluster (blue square points) far into the upper right-hand corner, with a point cluster CM located 

at 0.979 and 0.978. As previously stated (and is now clearly observable in this last return map), 

the introduction of IO stress into this last scenario variant has minimal impact on perpetuated 

BSAR levels, where the point cluster CM now only degrades to 0.953 and 0.952. 

 * All return map data are collected up to and including simulation time cycle 41 to reflect the maximal time window of 

nonzero Red combat agent survival levels observed across the Fombler’s Ford scenario variants. 
81

Figure 51. BSAR return map for the Fombler’s Ford simulation, with UGS density 

= 0.55, Blue fire point = 10 cells, and 1 Blue shot/cycle. 

Figure 52. BSAR return map for both benign environment (blue squares) and IOstressed (red crosses) Fombler’s Ford simulations, with UGS density 

= 0.75, Blue fire point = 10 cells, and 1 Blue shot/cycle. 
82

Figure 53. BSAR return map for both benign environment (blue squares) and IOstressed (red crosses) Fombler’s Ford simulations, with UGS density 

 = 1.00, Blue fire point = 17 cells, and 3 Blue shots/cycle. 

4. Conclusions 

In this research, a CA-based software model of IO-stressed network-centric battle units has been 

developed and demonstrated. The resulting model serves to demonstrate both coevolutionary 

unit configuration optimization of sparring Blue/Red network-centric combat units 

(combat_ga/GA combination) and enhanced network-centric operations of a Blue combat unit 

in a defensive posture against Red invaders (combat_proto). In the former case, the 

nonconvergence of fitness levels is due (at least in part) to the limited network-centric 

functionality built into combat_ga, where Blue’s access to greater communication ranges still 

doesn’t guarantee that all Blue combat agents will always share the same collective situational 

“picture” of the battlespace. The combined CA/GA model does, however, demonstrate 

coevolutionary exploration of different combat agent classes based on hardware capability and 

behavioral configurations. In the case of the enhanced CA simulation engine, combat_proto

serves to quantitatively demonstrate network-centric operations of a Blue combat unit in a 

defensive posture against Red invaders. In particular, the enhanced CA engine illustrates the 

operational impact of IO stress on Blue unit performance and the ways in which Blue can 

maintain operational robustness when encountering such stress. 
83

Given that the combat agents used within the CA models are reactive in nature (with limited 

decision-making capability and no explicit “commander” type of agent able to formulate new 

tactical strategies when operationally required), the next step in model development is to provide 

agents with more deliberative behaviors, thus providing a framework for the hierarchical C2 

decision-related structures ubiquitous to all military combat units. Current research into building 

more complex varieties of decision-making military command agents (Zhang et al. 2001) as well 

as hostile terrorist-style agents operating within densely-populated urban areas (Harper 2000) 

can provide a foundation for designing deliberative combat agents. Further work is still required, 

however, in order to realize rapidly adaptive agents operating within a network-centric combat 

structure. 
84

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by G. Yonas, Orlando, FL, pp. 266–280, 21–25 April 1997. 

Funes, P., and J. B. Pollack. “Measuring Progress in Coevolutionary Competition.” From 

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Haider, E. D. “Unattended Ground Sensors and Precision Engagement.” Master’s thesis, Naval 

Postgraduate School, Monterey, CA, September 1998. 

Harper, K. A., S. S. Ho, and G. L. Zacharias. “Intelligent Hostile Urban Threat Agents for 

MOUT Operations.” Proceedings of the 9th Conference on Computer Generated Forces and 

Behavior Representation, Orlando, FL, 2000. 

Hencke, R. B. “An Agent-Based Approach to Analyzing Information and Coordination in 

Combat.” Master’s thesis, Naval Postgraduate School, Monterey, CA, September 1998. 

Herman, M., and R. Hayes. “Measuring the Effects of Network-Centric Warfare.” Prepared for 

the Office of the Secretary of Defence, Net Assessment, Contract DASW01-94-D-0043, 

Booz•Allen & Hamilton, pp. v–vi, April 1999. 
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Hillis, D. P, and R. P. Winkler. “The Fox and the Hare: a Coevolutionary Approach to Course 

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Ilachinski, A. “Irreducible Semiautonomous Adaptive Combat (ISAAC): An Artificial-Life 

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Maes, P., (ed). Designing Autonomous Agents: Theory and Practice From Biology to 

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87

Appendix. Blue/Red Genome Classification for the Coevolutionary 

Simulation 

In this appendix, plots of the coevolutionary dynamics of both Blue and Red combat unit 

genomes across 1360 successive generations are presented in Figures A-1 through A-19. In 

these plots, the coevolutionary dynamics are expressed relative to genome membership in 

various sets of combat agent classes (Tables A-1 through A-19) based on one or more 

chromosome genes. In all, 19 different types of combat agent classes are defined (based on the 

23 chromosome genes preconfigured as mutable in the Blue/Red coevolution example presented 

in section 3.1.6), with a table summarizing the agent classes relative to a gene (or combination of 

genes) and associated plot of population per class (partitioned into Blue and Red agent 

populations) as a function of coevolutionary generation. In the cases where gene values are 

partitoned into a set of classes defined by very low, low, moderate, high, and very high levels, 

allele ranges per class are proportional to the unit interval partition used by Guzie for 

vulnerability risk assessment.1

 1 Guzie, G. L. “Vulnerability Risk Assessment.” ARL-TR-1045, U.S. Army Research Laboratory, White Sands Missile 

Range, NM, pp. 21–32, June 2000. 
88

 

(a) 

 

(b) 

Figure A-1. Coevolutionary dynamics of firepower range classes with class population (y-axis) 

vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
89

 

(a) 

 

(b) 

Figure A-2. Coevolutionary dynamics of single-shot Phit classes with class population (y-axis) 

vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
90

 

(a) 

 

(b) 

Figure A-3. Coevolutionary dynamics of firepower strength classes with class population (y-axis) 

vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
91

 

(a) 

 

(b) 

Figure A-4. Coevolutionary dynamics of firepower round limit classes with class 

population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and 

(b) Red unit. 
92

 

(a) 

 

(b) 

Figure A-5. Coevolutionary dynamics of armor strength classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
93

 

(a) 

 

(b) 

Figure A-6. Coevolutionary dynamics of sensor range classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
94

 

(a) 

 

(b) 

Figure A-7. Coevolutionary dynamics of carried land mine classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
95

(a) 

(b) 

Figure A-8. Coevolutionary dynamics of carried RF jammer bomb classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
96

 

(a) 

 

(b) 

Figure A-9. Coevolutionary dynamics of mine/jammer bomb deployment probability with 

class population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit 

and (b) Red unit. 
97

(a) 

(b) 

Figure A-10. Coevolutionary dynamics of signature classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
98

 

(a) 

 

(b) 

Figure A-11. Coevolutionary dynamics of crew classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
99

(a) 

 

(b) 

Figure A-12. Coevolutionary dynamics of maximum speed classes with class population (yaxis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
100

(a)

 

(b)

Figure A-13. Coevolutionary dynamics of transportation mode classes with class population (yaxis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
101

 

(a) 

 

(b) 

Figure A-14. Coevolutionary dynamics of attack initiation classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
102

 

(a) 

 

(b) 

Figure A-15. Coevolutionary dynamics of friendly attraction/repulsion classes with class 

population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) 

Red unit. 
103

(a) 

 

(b) 

Figure A-16. Coevolutionary dynamics of enemy attraction/repulsion classes with class population 

(y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
104

 

(a) 

 

(b) 

Figure A-17. Coevolutionary dynamics of friendly territory attraction/repulsion classes with 

class population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit 

and (b) Red unit. 
105

 

(a) 

 

(b) 

Figure A-18. Coevolutionary dynamics of enemy territory attraction/repulsion classes with class 

population (y-axis) vs. coevolutionary generation (x-axis): (a) Blue unit and (b) 

Red unit. 
106

 

(a) 

 

(b) 

Figure A-19. Coevolutionary dynamics of agitation classes with class population (y-axis) vs. 

coevolutionary generation (x-axis): (a) Blue unit and (b) Red unit. 
107

Table A-1. Combat agent classes based on gene G0 (firepower range). 

Combat Agent Class Gene G0 (Firepower Range) 

no firepower 0 

very short-range firepower 1–5 cells 

short-range firepower 6–10 cells 

Table A-2. Combat agent classes based on gene G1 (single-shot Phit). 

Combat Agent Class Gene G1 (Single-Shot Phit) 

very low Phit 0 < G1 < 0.20 

low Phit 0.20 < G1 < 0.40 

moderate Phit 0.40 < G1 < 0.60 

high Phit 0.60 < G1 < 0.80 

very high Phit 0.80 < G1 < 1.00 

Table A-3. Combat agent classes based on gene G2 (firepower strength). 

Combat Agent Class Gene G2 (Firepower Strength) 

very low-fire strength 0 < G2 < 4 

low-fire strength 4 < G2 < 8 

moderate-fire strength 8 < G2 < 12 

high-fire strength 12 < G2 < 16 

very high-fire strength 16 < G2 < 20 

Table A-4. Combat agent classes based on gene G3 (firepower number of rounds). 

Combat Agent Class Gene G3 (Firepower Number of Rounds) 

very low-fire limit 0 < G3 < 51 

low-fire limit 51 < G3 < 102 

moderate fire limit 102 < G3 < 153 

high-fire limit 153 < G3 < 204 

very high-fire limit 204 < G3 < 255 

Table A-5. Combat agent classes based on gene G4 (armor strength). 

Combat Agent Class Gene G4 (Armor Strength) 

very low-armor strength 0 < G4 < 4 

 low-armor strength 4 < G4 < 8 

moderate-armor strength 8 < G4 < 12 

high-armor strength 12 < G4 < 16 

very high-armor strength 16 < G4 < 20 
108

Table A-6. Combat agent classes based on gene G5 (sensor range). 

Combat Agent Class Gene G5 (Sensor Range) 

very short-range sensor 0 < G5 < 8 cells 

short-range sensor 8 cells < G5 < 16 cells 

medium-range sensor 16 cells < G5 < 24 cells 

long-range sensor 24 cells < G5 < 32 cells 

very long-range sensor 32 cells < G5 < 40 cells 

Table A-7. Combat agent classes based on gene G11 (carried land mines). 

Combat Agent Class Gene G11 (Carried Land Mines) 

very few land mines 0 < G11 < 51 

few land mines 51 < G11 < 102 

moderate number of land mines 102 < G11 < 153 

many land mines 153 < G11 < 204 

very many land mines 204 < G11 < 255 

Table A-8. Combat agent classes based on gene G12 (carried RF jammer bombs). 

Combat Agent Class Gene G12 (Carried RF Jammer Bombs) 

very few jammer bombs 0 < G12 < 51 

few jammer bombs 51 < G12 < 102 

moderate number of jammer bombs 102 < G12 < 153 

many jammer bombs 153 < G12 < 204 

very many jammer bombs 204 < G12 < 255 

Table A-9. Combat agent classes based on gene G14 (probability of mine/jammer bomb deployment in cell). 

Combat Agent Class Gene G14 (Probability Mine/Jammer Bomb Deployment) 

very low Pdeploy 0 < G14 < 0.10 

low Pdeploy 0.10 < G14 < 0.40 

moderate Pdeploy 0.40 < G14 < 0.60 

high Pdeploy 0.60 < G14 < 0.90 

very high Pdeploy 0.90 < G14 < 1.00 

Table A-10. Combat agent classes based on gene G15 (signature). 

Combat Agent Class Gene G15 (Signature) 

very low visibility 0 < G15 < 0.10 

low visibility 0.10 < G15 < 0.40 

moderate visibility 0.40 < G15 < 0.60 

high visibility 0.60 < G15 < 0.90 

very high visibility 0.90 < G15 < 1.00 
109

Table A-11. Combat agent classes based on gene G16 (number of human crews). 

Combat Agent Class Gene G16 (Number of Crews) 

no crew 0 

one crewmember 1 

two crewmembers 2 

three crewmembers 3 

more than three crewmembers > 3 

Table A-12. Combat agent classes based on gene G17 (maximum speed). 

Combat Agent Class Gene G17 (Maximum Speed) 

immobile 0 

very slow 1 – 2 

slow 3 – 4 

moderate 5 – 6 

fast 7 – 8 

very fast 9 – 10 

Table A-13. Combat agent classes based on gene G18 (mode of transportation). 

Combat Agent Class Gene G18 (Mode of Transportation) 

ground vehicle 0 (signifying a ground-based agent) 

air vehicle 1 (signifying an air-based agent) 

Table A-14. Combat agent classes based on gene G20 (probability of initiating an attack). 

Combat Agent Class Gene G20 (Probability Initiating Attack) 

very low Pinitiate attack 0 < G20 < 0.10 

low Pinitiate attack 0.10 < G20 < 0.40 

moderate Pinitiate attack 0.40 < G20 < 0.60 

high Pinitiate attack 0.60 < G20 < 0.90 

very high Pinitiate attack 0.90 < G20 < 1.00 

Table A-15. Combat agent classes based on a combination of genes G21 (friendly attraction) and G22 (friendly 

repulsion). 

Combat Agent Class Genes G21 and G22 (Friendly Attraction/Repulsion) 

collaborative G21 > 0.50 and G22 < 0.50 

noncollaborative G21 < 0.50 and G22 > 0.50 

indecisive G21 > 0.50 and G22 > 0.50 

uninterested G21 < 0.50 and G22 < 0.50 

Table A-16. Combat agent classes based on a combination of genes G23 (enemy attraction) and G24 (enemy 

repulsion). 

Combat Agent Class Genes G23 and G24 (Enemy Attraction/Repulsion) 

noncovert G23 > 0.50 and G24 < 0.50 

covert G23 < 0.50 and G24 > 0.50 

indecisive G23 > 0.50 and G24 > 0.50 

unconcerned G23 < 0.50 and G24 < 0.50 
110

Table A-17. Combat agent classes based on a combination of genes G25 (friendly territory attraction) and G26

(friendly territory repulsion). 

Combat Agent Class 

Genes G25 and G26 (Friendly Territory 

Attraction/Repulsion) 

defensive G25 > 0.50 and G26 < 0.50 

nondefensive G25 < 0.50 and G26 > 0.50 

indecisive G25 > 0.50 and G26 > 0.50 

unconcerned G25 < 0.50 and G26 < 0.50 

Table A-18. Combat agent classes based on a combination of genes G27 (enemy territory attraction) and G28 (enemy 

territory repulsion). 

Combat Agent Class 

Genes G27 and G28 (Enemy Territory 

Attraction/Repulsion) 

invasive G27 > 0.50 and G28 < 0.50 

noninvasive G27 < 0.50 and G28 > 0.50 

indecisive G27 > 0.50 and G28 > 0.50 

uninterested G27 < 0.50 and G28 < 0.50 

Table A-19. Combat agent classes based on gene G29 (probability of agitation). 

Combat Agent Class Gene G29 (Probability of Agitation) 

very low Pagitation 0 < G29 < 0.10 

low Pagitation 0.10 < G29 < 0.40 

moderate Pagitation 0.40 < G29 < 0.60 

high Pagitation 0.60 < G29 < 0.90 

very high Pagitation 0.90 < G29 < 1.00 
111

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1. REPORT DATE (DD-MM-YYYY)

February 2003 

2. REPORT TYPE 

Final 

3. DATES COVERED (From - To) 

October 2000–December 2001 

5a. CONTRACT NUMBER 

5b. GRANT NUMBER 

4. TITLE AND SUBTITLE 

Agent-Based Modeling of a Network-Centric Battle Team Operating Within an 

Information Operations Environment

5c. PROGRAM ELEMENT NUMBER 

5d. PROJECT NUMBER 

622618H80

5e. TASK NUMBER 

6. AUTHOR(S) 

Brian G. Ruth and J. Dana Eckart*

5f. WORK UNIT NUMBER 

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

U.S. Army Research Laboratory 

ATTN: AMSRL-SL-EA 

Aberdeen Proving Ground, MD 21005-5068

8. PERFORMING ORGANIZATION 

 REPORT NUMBER 

ARL-TR-2913 

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11. SPONSOR/MONITOR'S REPORT 

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Approved for public release; distribution is unlimited. 

13. SUPPLEMENTARY NOTES 

*Virginia Bioinformatics Institute 

1880 Pratt Drive, Bldg. XV 

Blacksburg, VA 24061

14. ABSTRACT 

A model developed to analyze the emergent behavior of a network-centric battle team undergoing hostile information operations 

(IO) stress events is presented. Networked battlefield platforms are modeled as mobile semiautonomous agents that operate 

within a cellular automata (CA) lattice. The CA form a discrete spatially extended dynamical system consisting of a parallel 

networked lattice of computational cells in two dimensions. A software framework that combines CA-based agents with a 

genetic algorithm was developed in order to explore the dynamics of two opposing but "coevolving" units of networked combat 

agents. Simulation results using two variants of the CA-based combat agent model, both of which include IO stress in the form 

of radio frequency communications jamming, are analyzed and discussed. 

15. SUBJECT TERMS

cellular automata, agent-based model, genetic algorithm, network-centric warfare, information operations 

16. SECURITY CLASSIFICATION OF: 

19a. NAME OF RESPONSIBLE PERSON

Brian Ruth 

a. REPORT 

UNCLASSIFIED 

b. ABSTRACT 

UNCLASSIFIED

c. THIS PAGE 

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