Computer Systems Lab TJHSST Current Projects 20042005 Second Period

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Computer Systems LabTJHSSTCurrent Projects
2004-2005Second Period
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Current Projects, 2nd Period

• Casey Barrett The Design and Implementation of
  Decicion Trees for Career Guidance
• Andy Desoto Modeling of Evacuation Centers using
  NetLogo
• Keith Diggs Optimizing Genetic Algorithms for
  Use in Cyphers

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Current Projects, 2nd Period

• John Fitzsimmons Creating a 3D Game With
  Textures and Lighting
• Christopher Goss Paintball Frenzy Graphical
  Turn-Based Game With a Minimax AI Agent
• Stephen J. Hilber Modeling Evolving Social
  Behavior
• Rachel Miller Use of Machine Learning to Develop
  a Better Artificial Intelligence for Playing
  Tic-Tac-Toe

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Current Projects, 2nd Period

• Kyle Moffett The Analysis and Maintenance of a
  Robust, Highly- Available Computer Systems
  Laboratory
• Jason Pan Developing an AI Player for "Guess
  Who?"
• Madeleine E. R. Pitsch Study of Creating
  Compuattional Models of Traffic
• Timothy Wismer Development of a
  Deadlock-Detecting Resource Locking Algorithm for
  a Kernel Debugging User-space API Library (KDUAL)
• Dan Wright Developing Algorithms for
  Computational Comparative Diachronic Historical
  Linguistics

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Decision Trees for Career GuidanceThis research
project will be an investigation into the design
and implementation of various decision trees for
career guidance. A decision tree takes into
account some sort of situation outlined by a
group of parameters and outputs a Boolean
decision to the situation. This project will take
into account many aspects associated with
decision trees including database building,
searching and sorting, and algorithms for
accessing data.My project utilizes numerous
decision trees in an effort to serve as a tool
for career guidance for young adults. A user will
fill out a form of specified fields that will
then be analyzed by the group of decision trees
until a field of study/occupation is given to the
user as the outcome. This group of decision trees
will be built through database building
techniques.
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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• Abstract
• This research pro ject will be an investigation
  into the design and implementation of various
  decision trees for career guidance. A decision
  tree takes into account some sort of situation
  outlined by a group of parameters and outputs a
  Boolean decision to the situation. This pro ject
  will take into account many aspects associated
  with decision trees including database building,
  searching and sorting, and algorithms for
  accessing data. My project utilizes numerous
  decision trees in an effort to serve as a tool
  for career guidance for young adults. A user will
  fill out a form of specified fields that will
  then be analyzed by the group of decision trees
  until a field of study/occupation is given to the
  user as the outcome.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• This group of decision trees will be built
  through database building techniques. I have
  utilized extensive research from various websites
  that offer expertise in a number of areas. I used
  tutorial websites that had examples and
  explanations of decision trees generated by the
  C4.5 program. I have also tried to read various
  research papers that deal with decision trees.
  Although I have yet to find one that remotely
  relates to career guidance, I feel that my
  understanding of decision trees has increased as
  a result of these research papers.
• Introduction
• This pro ject will utilize numerous decision
  trees to assist young people by helping them
  focus on their interests and what career paths
  may coincide with those interests.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• Background/Implementation
• A decision tree is a graphical representation of
  the decision analysis process. This type of tool
  consists of some sort of input, whether it is a
  situation or an ob ject. This input is then sent
  through a set of parameters, or rules, and
  eventually the tree gives a Boolean output. There
  are many different types of parameters that can
  be used. Or in other words, many different types
  of parameter cases can be used. These cases can
  include numerical data, simple yes/no answers, or
  word answers, such as hair color (black, brown,
  or blonde). Each parameter will have a specified
  set of cases that correspond to the parameter.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• To help me better understand decision trees and
  how to build them, I have utilized Professor Ross
  Quinlans revolutionary decision tree generator
  programs, the C4.5. What these programs do is
  take into account large sets of data (from
  properly written database files) and looks for
  correlations within the sets. It then uses these
  correlations to build a decision tree that
  follows the 'rules' outlined by the correlations.
  In an auxiliary program, the C4.5rules program,
  the 'rules' for the tree generated can be
  displayed. This program gave me a better
  understanding of how decision trees are built. I
  wrote my own database files that could be read by
  the C4.5 program. I learned the proper syntax for
  these database files, which I would later utilize
  for career guidance.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• For each database, two separate kinds of files
  are needed, the .names file, which outlines each
  parameter and the appropriate cases as well as
  the end cases, and also the .data file, which
  consists of singular entries. Each entry in the
  database properly fills up each parameter
  outlined in the .names file. I also had to
  research techniques for career guidance. The most
  rudimentary of these techniques was to have a
  user fill out a list of field data and then
  compare the user's answers to those of a highly
  comprehensive database. Some of these
  questionnaire-type devices included many
  different occupational fields, such as art/music,
  engineering, writing/journalism, and social
  services. For my pro ject, I decided to start out
  with two very distinct fields that would be a
  good way to acclimate myself for making a career
  guidance program.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• These two fields would be the liberal arts field
  and the sciences field. For career guidance, I
  separated the decision trees that I would need
  into three different and distinct trees. The
  first one is a tree designed to help a user
  decide whether they should focus on either of the
  broad intellectual categories of the sciences or
  the liberal arts. This decision will be decided
  based a series of fields the user fills out in a
  separate C program. Some of these fields
  include the user's grades in their current
  English and Math class, if the user is in a
  science club, the number of computers that the
  user owns, and the number of plays the user has
  participated in during the last year. These
  starting questions are somewhat broad because
  this is the first preset tree that the user will
  be compared to.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• The next step was to develop a user input program
  that prompts the user questions related to career
  guidance. The program then takes the answers and
  writes them to a .data file. I wrote this program
  in C. Likewise, I needed to use the fstream.h
  library in order to gain access to the classes
  ifstream and ofstream. I then created the
  questions test file that became the ifstream that
  the program will read. This program contained
  four functions, better outlined in the Iteration
  Report Third Quarter. Currently, the only
  acceptable inputs for this program are strings,
  however, during the fourth quarter, I will expand
  the inputs to include classes char, int, and
  double. The user input is first compared to a
  database of other people's answers and the
  decision that each of the other people decided
  upon.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• Since I have not had enough users, the databases
  are fictitious data that I have engineered in
  order to fit my ideal decision tree for this
  progression.
• The data from this database helps comprise a
  decision tree that the user's answers will be run
  through. Since there are three different decision
  trees, three separate ideal databases will be
  made, one for the broad first test, and then one
  each for the science fields and the liberal arts
  field. After a first decision is made, it will be
  relayed back to the user. If the user wishes to
  continue, he or she will be given another set of
  parameters that will be more in depth in either
  the liberal arts or science fields.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• These trees, which are still in progress, will
  help the user compare their answers against
  another database corresponding to their broad
  interests (liberal arts or sciences).
• When compared to these more specialized
  databases, a more focused decision was sent back
  to the user. This is because the user's responses
  will be matched up with the decision tree that
  the databases helped generate and the decisions
  that the databases output. Right now, I am
  working on the algorithm that compares the user
  input answers to the decision tree. This
  algorithm will produce some sort of numerical
  correlation between the sets of answers. The
  higher the correlation number is with respect to
  that specific path of the decision tree, then the
  more likely the program will output an answer
  similar to that of the higher correlation.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• In the end, a type of occupation or a field of
  study will be output to the user based on the
  correlations. Also, I have trained extensively in
  the ways of the Reverse Game. Without possessing
  a shred of natural Reverse Game ability, I have
  worked my way up from the ranks of novice to a
  somewhat respectable player capable of defeating
  each and every of the 20 progressively
  challenging levels.

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The Design and Implementation of Decicion Trees
for Career GuidanceCasey Barrett

• Conclusions References
• Decision Trees A subtopic of machine learning.
  http//www.aaai.org/AITopics/html/trees.html
  Quinlan, Ross. (n.d.) Ross Quinlan Personal Page.
  http//www.rulequest.com/Personal/ C4.5 Tutorial.
  http//www2.cs.uregina.ca/hamilton/courses/831/no
  tes/ml/dtrees/c4.5/tutorial.html Career Key Job
  Interests. http//www.careerkey.org/cgi-bin/ck.pl?
  actionchoices Hettich, Scott. (n.d.) UCI Machine
  Learning Repository http//www.ics.uci.edu/mlearn
  /MLRepository.html Nilsson, Nils. (May 10, 2004).
  Introduction to Machine Learning
  http//robotics.stanford.edu/people/nilsson/mlbook
  .html

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Modeling of Evacuation Centers Using
NetLogoModeling is a powerful tool that allows
a programmer or social engineer to observe
cause-and-effect relationships in occurences that
a) happen too slowly or quickly to see, b)
involve danger or safety concerns, c) occur on a
scale too large or too small for study, d) is not
a common occurrence. Using NetLogo, a
multi-agent programmable modeling environment,
the socio- and psychological factorsaffecting
decision-making in these situations can be
effectively simulated.
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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• Abstract
• The actions of citizens during terror and
  evacuation events are oftentimes hard to predict.
  Using NetLogo, a 'crossplatform multi-agent
  programmable modeling environment' from The
  Center for Connected Learning and Computer-Based
  Modeling (CCL), the socio- and psychological
  factors affecting decisionmaking in these
  situations can be effectively simulated. Through
  appropriate research in categories of modeling
  and sociology, human behavior can be studied to
  help urban developers and social engineers
  protect the nation's interest its citizens.
  Citizens that follow certain algorithms, or
  behave in certain ways, have a much greater
  chance of survival.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• I . Introduction to NetLogo
• NetLogo is a cross platform multi-agent
  programmable modeling environment created by
  Northwestern University's Center for Connected
  Learning and Computer-Based Modeling (hereafter
  CCL). It was designed specifically for simulation
  of social and natural phenomenon. Originally
  StarLogoT, this language and graphical user
  interface hybrid offers a multitude of
  possibilities for researchers and students,
  allowing users to examine interactions and
  behaviors on both micro and macro scales.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• NetLogo is designed with accessibility in mind
  the program allows for quick Java applet
  exportation and interactivity with CCL's HubNet
  interface, which lets the user run participatory
  simulations in which a class or group of testers
  takes part in enacting the behavior of a system
  as each user controls a part of the system by
  using an individual device (such as a TI-83
  calculator or a networked computer). NetLogo
  comes packaged with an expansive models library
  and an assortment of code samples for easy
  access. A. NetLogo Graphic User Interface
  NetLogo's main advantage is that it allows a
  quick and relatively simple way for the
  programmer to implement a convenient graphical
  user interface (hereafter GUI).

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• A NetLogo GUI can contain the following
  components buttons, sliders, switches, choices,
  monitors, plots, texts, and outputs. 1) Buttons
  Buttons activate on the user's command. There are
  two types of buttons, standard buttons and
  forever buttons. Standard buttons activate the
  contained code on click, but forever buttons
  execute the contained commands repeatedly until
  the button is clicked a second time, mimicking a
  whileloop.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• 2) Sliders Sliders allow the user to manually
  select the value of a certain variable. Sliders
  control the minimum and maximum values, as well
  as the increment of increase/decrease, while the
  user chooses the value. 3) Switches Switches
  allow for a user to directly affect a boolean
  value. 4) Choices Through the use of choices,
  the user can select certain values, Strings, and
  more that the programmer allows. 5) Monitors
  Monitors allow the user to examine the value of a
  variable as the simulation runs. 6) Texts and
  Outputs Texts and Outputs display certain
  messages the programmer encodes. These can be
  displayed at any time during the simulation.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• B. HubNet and Other Extensions NetLogo's
  versatility allows programs to be accessed and
  used in environments outside the computer
  laboratory setting. HubNet, one instance, allows
  a multitude of users to interact with a single
  model through the use of Texas Instruments TI-83
  calculators or networked computers. This is ideal
  for classroom settings because it allows each
  tester involved to affect the model in some way.
  Also, NetLogo offers easy exportation to Java
  Applet format, and with several keystrokes, an
  applet embedded in an HTML page can be created.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• I I . Introduction to Modeling
• Modeling is a powerful tool that allows a
  programmer or social engineer to observe
  cause-and-effect relationships in occurences that
  a) happen too slowly or quickly to see, b)
  involve danger or safety concerns, c) occur on a
  scale too large or too small for study or d) is
  not a common occurrence. Armed with this
  knowledge, a scientist can use the material
  learned to help a community, understand a cause,
  or solve a problem. A perfect use for the
  modeling technology of today is a subject that
  meets all of these above criteria.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• The modeling of population centers during
  evacuation situations is perfect for such a
  study, because evacuation frequently occurs too
  quickly, is quite dangerous, happens rarely, and
  happens on such a wide scale that observing
  patterns is nearly impossible. In order to
  accurately code such an event, certain features
  must be researched.
• A. The Environment In general, areas to be
  evacuated generally contain a large number of
  citizens in a small area the population density
  in such regions tends to be quite high. In the
  metropolises and cities of today, high-rise
  apartments, parking garages, subway stations all
  provide unique, but crowded, environments.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• These situations provide the greatest trouble to
  researchers and social engineers. B. The Citizens
  In times of terror or enormous stress, citizens
  often behave irrationally different than they
  would normally. Thus, citizens' behavior in the
  simulation must reflect this behavior. The
  intelligences programmed into version 1.2 follow.
  ) Speedy, Normal, Slow The Speedy, Normal, and
  Slow citizens differ in the number of times they
  move per timestep. Speedy, Normal, and Slow
  citizens more 3, 2, and 1 squares, respectively.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• When they collide with a wall (or another
  citizen, if Collision? is turned on), they turn
  in a random direction and continue their
  movement. 2) Righty Righty citizens react to
  collisions by turning ninety degrees. They move
  two squares per timestep. 3) Retreater
  Retreaters react to collisions by turning 180
  degrees, taking a step forward, and choosing a
  direction in their 90 degree front arc.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• I I I . Running the Simulation
• Utilizing the Simulation consists of three steps
  initializing the Basic Environment Setup, or BES
  running the model analyzing the model with the
  Graphical (Statistical) Interface, or GSI. A.
  Basic Environment Setup (BES) The user controls
  the following sliders, choices, and etc. to set
  the parameters for the environment. 1)
  'walldistancey' Slider sets the distance between
  the horizontal walls. When a citizen's movement
  would take it into a wall, it runs its collision
  program detailed above. The two horizontal walls
  each contain a passable gap separating the danger
  from the safety area.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• 2) 'walldistancex' Slider sets the distance
  between the vertical walls. When a citizen's
  movement would take it into a wall, it runs its
  collision program detailed above. 3)
  'evacuationtime' Slider sets how many timesteps
  the model runs for. 4) 'collide?' Switch selects
  whether or not citizens collide with one another,
  activating collision code upon impact. 5)
  'areasize' Selector selects how many citizens
  begin in your model.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• The selector has five options a) Metropolis (225
  citizens) b) City (150 citizens) c) Village d)
  Town (90 citizens) e) Thorpe (15 citizens) f)
  Abode (3 citizens). There are (citizens / 3)
  citizens of each color (red, white, blue) in the
  model. 6) 'explosiontime' Slider selects at what
  time the explosion occurs. The explosion is the
  radius selected below.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• 7) 'explosionrad' Slider chooses the radius of
  the explosion. Explosions kill citizens contained
  within them and creates an impenetrable wall. 8)
  'Draw Walls' Button while depressed, lets the
  user draw black walls 1 pixel in diameter. I V. T
  H E P RO J E C T This project was set up to
  determine the most effective ways to get large
  numbers of citizens to safety in times of danger.
  In order to determine how to do this, certain
  things must be determined. First, the parameters
  that add to the dangerousness of the environment
  should be measured. Secondly, once the situations
  of greatest danger are found, the most effective
  intelligences to escape this danger must be
  poinpointed as well.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• Using the environment instantiated in the setup
  (see subsection "The Environment"), different
  situations are tested. The tests were run in
  environments of four different dimensions large
  x large, short x large, large x short, and short
  x short. V. T H E T E S T As described above (see
  "Project" section), the first part of the test is
  to determine the dangerous situation. The results
  of this test follow.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• These tests were ran with build version 1.2,
  located on the web www.tjhsst.edu/
  kdesoto/techlab. Large x large 15m x 5m Short x
  large 5m x 15m Large x short 15m x 5m Short x
  short 5m x 5m A set of 225 "control" citizens
  was given a period of 120 minutes to escape each
  danger situation. This was performed ten times,
  and escaped citizens were graphed. This can be
  found in the appendix. The l x l area, on
  average, trapped almost 111 citizens per 120
  minutes. This is almost 50 percent. The s x l
  area, on average, trapped almost 22 citizens per
  120 minutes. This is almost 10 percent.

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Modeling Evacuation of Population Centers Using
NetLogoKurt Andrew DeSoto

• The l x s area, on average, trapped almost 27
  citizens per 120 minutes. This is almost 12
  percent. The s x s area, on average, trapped 0
  citizens per 120 minutes, 0 percent. VI.
• Conclusion The conclusion goes here.
• V I I . Acknowledgement
• The authors would like to thank... Mr. Randy
  Latimer, Computer Systems Laboratory Mr. Uri
  Wilenski, CCL REFERENCES
• A 1 H. Kopka and P. W. Daly, A Guide to LTEX,
  3rd ed. Addison-Wesley, 1999.

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Optimizing Genetic Algorithms for Cypher
DecodingOver the past several years, genetic
algorithms have come into wide use because of
their ability to find good solutions to computing
problems very quickly. They imitate nature by
crossing over strings of information represented
as chromosomes, with preference given to the more
fit solutions produced. They hold great promise
in the field of cryptology, where they may be
used to quickly find good partial solutions, thus
eliminating much of the intense manual labor that
goes into identifying initial coding schemes.
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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Abstract
• Over the past several years, genetic algorithms
  have come into wide use because of their ability
  to find good solutions to computing problems very
  quickly. They imitate nature by crossing over
  strings of information represented as
  chromosomes, with preference given to the more
  fit solutions produced. They hold great promise
  in the field of cryptology, where they may be
  used to quickly find good partial solutions, thus
  eliminating much of the intense manual labor that
  goes into identifying initial coding schemes.

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Introduction
• This document is the written history of the
  author's work on genetic algorithms over the
  course of the past year. The hope is to reach a
  better understanding of genetic algorithms
  through the results of intensive work on the sub
  ject in several different applications.
• The Problem
• Genetic algorithms have become very useful tools
  in the field of computer science, however, they
  are rather open-ended and can be applied in
  wildly different ways.1 The problem here is to
  optimize a genetic algorithm for a cryptological
  problem.

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Statement of Objective
• The ultimate objective is to use a genetic
  algorithm to efficiently solve different problems
  such as the Beale cypher and the evolution of pi.
  Regardless of whether that objective is reached
  or not, the pro ject should foster a better
  understanding of how to apply genetic algorithms
  to cryptology problems. The purpose of this
  report is to document the development and
  effectiveness of the project.

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Scope
• The pro ject deals mainly with the Beale Cyphers
  as a tool with which to determine which
  properties of a genetic algorithm may be modified
  so that the algorithm may operate more smoothly.
  The pro ject also includes an initial foray into
  evolving the value of pi.
• Background
• Historical Summary
• The Beale Cypher is one of the most famous
  unsolved puzzles in cryptography. Basically, 100
  years ago, Beale buried treasure in a lake in
  Bedford County, near Roanoke.

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Three letters were written, encoded and given to
  a friend. Beale subsequently travelled west and
  never returned. Later, one of the 3 letters was
  deciphered. Beale had used a simple encoding
  algorithm. His letters consisted of a large list
  of numbers. These numbers corresponded to the
  first 480 words in the American Declaration of
  Independence. For example, if the document starts
  "The quick brown fox..." then 3 would represent
  the letter 'b' and so forth.
• (1 Duray)

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• So, one of the letters was succesfully
  deciphered, but to this day the other two remain
  unbreakable. While there is a fair amount of
  evidence that the Beale treasure is a hoax, there
  is no reason to believe that the cyphers
  themselves are. It is also probable that the
  other cyphers either use the same document, just
  different encoding methods, or use similar
  documents and the same encoding.2
• Preceding Work
• I am not sure how much work has been done trying
  to decode the two as-yet undeciphered letters.

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Throughout the 1970s, one Dr. Carl Hammer used
  computers to analyze those two letters and found
  cyclic patterns in the numbers that suggested an
  actual coding mechanism as opposed to randomly
  chosen numbers.3 There is some evidence that the
  cypher is nothing more than a hoax. Kenneth
  Dobyns concludes after a lengthy statistical
  analysis that "there is no content to the
  cyphers" and that the numbers in the two
  undeciphered texts were chosen "substantially at
  random, limited only by the restriction that no
  numerical value would be repeated immediately
  adjacent or semi-adjacent to a similar value."

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Theory
• It is one of the great ironies of computer
  science that a machine built to outperform a
  living organism works best at times when it
  imitates the living organism itself. Charles
  Darwin's 1859 book On the Origin of Species laid
  the foundation not only for anthropology but for
  a great number of largely economic theories such
  as "Social Darwinism." The idea that the members
  most fit to survive would be the most likely to
  reproduce resounded in the scientific community.
  It should come as no surprise, then, that
  computers might be able to produce end results as
  well as nature itself if they would follow this
  model.

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• A genetic algorithm is a method of computing that
  attempts to replicate nature's mechanism for
  evolution by organizing computer data into
  formats that are akin to DNA chromosomes. The
  idea is to have multiple manifestations of these
  computer data so that they might "cross over" the
  way that real-life chromosomes do. The "crossing
  over" does not happen randomly the programmer
  has to code the program to do this, but he can
  choose to do it in a manner that takes two
  promising sets of data and combines them into
  what would hopefully be an even better set of
  data. We need these "promising sets of data"
  because the data are supposed to be the solution
  to some sort of problem.

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Genetic algorithms are most effective in quickly
  finding good solutions to problems in which there
  may be several acceptable solutions.
• The problem of decoding the Beale Cypher, then,
  would seem to be an excellent candidate for a
  genetic algorithm as described above. The search
  space is essentially infinite, yet it would be
  possible to program the computer to recognize
  good solutions as those with a high incidence of
  actual English words coming out of the
  translation. While there is one "correct"
  solution, but if I can get my program to come up
  with a solution that a human viewer could
  identify as reasonably close to this correct
  solution, then it will have been a success.
• 2 Matthews 3 Krystek

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Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Design Criteria
• Primary Criteria
• The pro ject will be able to take the code of
  the second letter in the Beale Cypher and
  independently come up with a string in which an
  observing human can recognize the English message
  of the cypher. The pro ject will use a genetic
  algorithm to do so, crossing over possible
  solutions to produce hopefully better ones.
• Secondary Criteria
• The pro ject may independently produce the
  exact answer to the second letter in the Beale
  Cypher, character for character. The genetic
  algorithm may be both time-efficient and
  space-efficient, allowing for easy application to
  other similar cyphers (even other letters)

47
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• The pro ject may produce a viable solution to the
  first and third letters of the Beale Cypher from
  which a human would be able to do meaningful and
  independent work.
• A genetic algorithm similar to the one used to
  satisfy the Primary Criteria may be adapted to
  other areas in cryptology and (this is a long
  shot) into other research areas such as
  artificial life.
• Procedure
• The project largely dictated its own future
  since there was no initial goal at the outset of
  the year, the pro ject was largely defined as
  whatever might be successful.

48
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• At the time the author applied for the Computer
  Systems Research Lab, his idea was to create an
  intelligent computer agent to play the Japanese
  game Go. By September, the idea of exploring
  genetic algorithms had become fairly entrenched
  in the author's mind, but a search for
  applications was still necessary. After spending
  two months tinkering with a program that evolved
  the value of , it was decided to pursue the
  Beale Cypher in depth. The program decode01.c was
  written to the point where it would begin
  crossing multiple chromosomes over each other and
  producing results that clearly followed
  progressive patterns (thanks to substantive
  debugging). Once the functionality for crossing
  over was written into the program, work on the
  heuristic could begin.

49
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• It is based on the relative frequencies of each
  alphabetical character in the English language4
  and the most commonly occurring words in the
  language.5 The heuristic analyzes the
  translations produced by a chromosome and
  calculates how far the relative character
  frequencies of the translations deviate from that
  of general English, with priority values being
  assigned to the translations that deviate the
  least in terms of character frequency and also
  have the most identifiable English words.

50
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• The word-search function was the most difficult
  part of the program to write, not because the
  goal was uncertain, but because it introduced an
  entirely new problem to the pro ject that of
  organizing an extensive list of data into the
  program's infrastructure in such a manner that it
  could then be successfully used to analyze
  translations of the program. I finally settled on
  a linked-list structure after a long period of
  flailing with a very error-prone
  multi-dimensional array.

51
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Results
• Results at this point are very basic I have just
  recently completed the word-search function and
  have yet to use it heavily in tandem with the
  genetic algorithm. Appendix 2 shows some sample
  results. The first part demonstrates the
  character-frequency analysis function it shows
  five false translations of the Beale cypher and
  gives their variances. (Defined in the program as
  the sum of the percentage points by which the
  frequency of each letter relative to the complete
  translation varied from the ideal percentage
  defined in Singh. A lower number is better.) The
  second part shows the word-search function, only
  recently completed.

52
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• The results shown include the debugging code I
  planted in the program. It shows the program
  going through the sample translation two
  characters at a time. Every exclamation point
  means that the program has identified the most
  recently displayed two-character combination as
  possibly beginning a word. Actual words showing
  up after exclamation points indicate that that
  word has been identified in the text and that the
  program has added a point to the translation's
  heuristic score.
• References
• Dobyns, Kenneth. "Beale Codes Were they a
  Hoax?" Genealogy, History, and Miscel laneous
  Material. 1984. 25 Jan. 2005 www.myoutbox.net/bea
  lhome.htm
• 4 Singh, accessed at Southampton

53
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• Duray, Naranker. "Genetic Algorithms." Imperial
  Col lege of London Department of Computing.
  1996. 25 Jan. 2005 www.doc.ic.ac.uk/nd/surprise
  96/journal/vol4/tcw2/report.html Gantovnik,
  V.B., Z. Gurdal, and L.T. Watson. "Genetic
  Algorithm with Memory for Optimal Design of
  Laminated Sandwich Composite Site Panels."
  Technical Report, 2002. Computer Science _at_
  Virginia Tech. 21 May 2002. Virginia Polytechnic
  Institute and State University. 8 Feb. 2005

54
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• eprints.cs.vt.edu8000/archive/00000555/01/gaCS02
  a.ps Huss, Eric. "The C Library Reference
  Guide." Webmonkeys A Special Interest Group at
  the University of Il linois. 1997. 14 Apr. 2005
  www.acm.uiuc.edu/webmonkeys/book/c guide/
  Krystek, Lee. "The Beale Cryptograms." The Museum
  of Unnatural History. 2000. 13 Jan. 2005
  www.unmuseum.org/beal.htm Matthews, James. "The
  Beale Cypher." Generation5. 2003. 13 Jan. 2005

55
Optimizing Genetic Algorithms for Use in
CyphersKeith Diggs

• www.generation5.org/content/2003/beale.asp
  Singh, Simon. The Code Book The Secret History
  of Codes and Code-breaking. Unknown Fourth
  Estate, 1999. University of Southampton, ed.
  Teachers' Notes to Accompany the Lesson Packs.
  National Cipher Challenge. Southampton, UK
  University of Southampton, 2003. National Cipher
  Chal lenge. 1 Oct. 2003. University of
  Southampton. 25 Jan. 2005 www.cipher.maths.soton.
  ac.uk "The 500 Most Commonly Used Words in
  English." World English Test, Learn, and Study
  the English Language Online. 22 Aug. 2001. 25
  Jan. 2005. www.world-english.org/english500.htm

56
Creating a 3D Game With a Study of OpenGL
Textures and Lighting TechniquesTo create a
first person 3D game using OpenGL. The program
consists of using models, textures, lighting, and
polygons to create a 3D world in OpenGL. Various
equations are used to calculate camera angles,
movement, and physics. For example, to move the
camera, eye movements are controlled by
glLookAt, which takes an eye position with 3
points (x,y,z) and 2 vectors. One vectors is the
up direction and the other is the forward
direction.
56
57
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• Abstract
• The objective is to create a simple, 3D first
  person shooter using OpenGL and C. To acheive
  this goal, I will need to implement textures,
  lighting, 3Dshapes, polygons, and basic C
  structures. II. Introduction The problem of
  creating a 3D first person shooter from scratch
  is a large task. To accomplish this task, I am
  breaking down the problem into simple blocks,
  such as getting a camera to work so that I can
  view the world from a person's perspective and
  move around.

58
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• The reason I chose this pro ject is because
  computer games interest me immensly and creating
  one of my own would give me some experience and
  pleasure, even if it is very primitive. My pro
  ject will probably be finished very near the end
  of the year as there will always be another
  upgrade to add. The maximum scope of this program
  would be to have multiple users playing on the
  same computer or over a network and models of the
  users and different ob jects. Multiple weapons
  would be available to pick up and different
  levels to play. However, the multiple user aspect
  nor the models will be accomplishable by the end
  of the year.

59
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• III. Theory and Discusion and Workplan
• I will go over the two main features that I have
  completed so far the camera and collisions. The
  camera is the user's point of view. Using
  keyboard input to change velocity, the user moves
  around. The largest task was create a realistic
  system to use mouse input to rotate the camera
  up, down, and side to side. To do this,
  horizontal and vertical movement is calculated in
  pixels for each iteration and used to modify two
  angles. One for up and down and the other for
  side to side.

60
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• Using both of these angles, I calculate a unit
  vector in the opposite direction the user is
  looking to input into an openGL function. The
  distance the user looks up is the sine of the
  angle up. The maximum distance forward is the
  cosine of this angle. The forward distance is
  further broken up into x and z components. The x
  component is the max distance multiplied by the
  sine of the angle to the side and the y component
  is the max distance multiplied by the cosine of
  that angle.

61
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• As a result, my code looks like such with a unit
  vector of length 2 temp2cos(angleDown)
  centerXeyeX-tempsin(angleLeft)//puts center
  point 2 units centerZeyeZ-tempcos(angleLeft)//i
  n front of the eye centerYeyeY-2sin(angleDown)
  An up vector is also generated very similar to
  this to make the camera right side up. The other
  large chunk of code is collisions. My collisions
  are very simple. When a pro jectile hits an ob
  ject, it stops and moves back a little (so it
  doesn't get stuck in the ob ject). The
  acceleration due to gravity then pulls the ob
  ject down. If it hits the ground or the top of an
  ob ject it just stops and stays.

62
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• After a certain amount of time, the pro jectile
  will be deleted. That way, there won't be
  hundreds of pro jectiles bogging down on
  processing power. To detect collisions, I check
  to see if the pro jectiles is inside the bounds
  of the ob ject plus the radius of the pro
  jectile. This currently only works for
  rectangular prisms or flat rectangles. Circles
  and spheres are also workable with this
  algorithm. To do more complicated ob jects,
  multiple smaller, undrawn ob jects will have to
  be used to approximate the larger shape and
  acheive accurate collisions.

63
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• Another large piece of the program will be
  textures. I will have to load in a texture for
  each different type of surface I am going to use.
  To do this I must read a bitmap image into
  memory. I do this by first reading in the bitmap
  header which contains information about the file,
  and then I read in 4 values for every pixel. Each
  value is the size of a byte, so I read these
  values into an array of unsigned chars. This
  array is then plugged into a texture function
  (glTexImage2D) which stores the image in video
  memory. I then delete the array from memory.

64
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• For some reason, the read in textures are not
  staying in video memory, even though I have
  debuged and found I interpreted the bitmap
  correctly. Textures will only be accomplished if
  time permits because I have decided to push back
  fixing the problem until later. The other main
  aspect of collisions are the actual ob jects. I
  have created a "class" ob ject and I have a
  pointer list of these ob jects. When a
  destroyable ob ject gets hit enough, it will be
  destroyed and I will take it out of the list so
  it isn't drawn and doesn't use processing power.
  Currently, I am working on a system to read in ob
  jects and put them in the list.

65
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• When I finish, I will be able to load in an
  environment from a text file, allowing for
  multiple levels. After accomplishing muliple text
  input levels, I will work on a HUD (heads up
  display) which will show life, time, and score. I
  will also add different weapons that cause
  different damages that the player can pick up in
  the level. Lighting will be added last, such as a
  muzzle flash effect and lighting throughout the
  level. My basic system of operations is to code,
  test, and debug. I do this for every single
  aspect of the pro ject. When I added the camera
  code, I had to fix alot of errors.

66
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• For example, I found out on the opengl.org site
  that the "center" values determine a point a unit
  away from the camera in the direction it is
  looking rather than a unit vector. The "up"
  values actually do determine a unit vector in the
  "up" direction. Only I am working on this pro
  ject. I need no money and therefore have no
  budget. The only equipment I am using is a
  computer, implementing OpenGl and C. I may also
  use a graphical modeler later on. I have mainly
  used the OpenGL website to get all of my
  information for my pro ject.

67
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• However, as I am now getting into some more
  complicated aspects (not just camera and drawing
  basic ob jects), I will need more and more
  sources. I have begun to research texturing,
  which I have researched outside the opengl.org
  site. I will have to research elsewhere for most
  of the remaining tasks as well. Tasks to be
  completed include adding textures, lighting,
  additional ob jects, and a more complicated
  environment.

68
Creating a 3D Game With Textures and Lighting
John Fitzsimmons

• Sources OpenGL - The Industry Standard for High
  Performance Graphics. http//www.opengl.org/.
  January 27, 2005. Spacesimulator.net - OpenGL
  Texture mapping. http//www.spacesimulator.net/tut
  3 texturemapping.htm January 26, 2005. OpenGL
  Texture Mapping An Introduction.
  http//www.gmonline.demon.co.uk/cscene/CS8/CS802.h
  tml. January 23, 2005.

69
Paintball Frenzy!Minimax Agent AIThe purpose
of this project is to create an innovative and
enjoyable graphical game and program a minimax AI
agent that performs optimally.
69
70
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• 0.1 Abstract
• Paintball Frenzy is a unique turn-based game of
  my creation. The game is played on a four by four
  grid layout that is initially a neutral grey
  color. Two to four players that are either human
  or computer controlled posess one colored
  paintball that originates at one of the board's
  four corners. The players take turns moving about
  the grid one horizontal or vertical space at a
  time.

71
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• Players also have the option to jump over one
  player's paintball in an adjacent space. There is
  a time limit that each player must choose their
  move before. Whenever a paintball moves into a
  space on the grid, that space becomes the color
  of that paintball. Whichever player has the most
  colored spaces at the end of the turn limit wins
  the game. My final pro ject was the creation of
  this game Paintball Frenzy. First, I had to
  invent the game and all of it's particularities
  and balance issues.

72
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• Of course, I had to program this game's
  framework, first with a standard terminal display
  and keyboard input, and later with graphical
  gameboard display and mouse controls.
  Additionally, I had to program several artificial
  intelligence agents for my game including an
  agent that moves randomly and an agent that
  performs optimally. To design the agent that
  moves optimally, I researched the minimax
  algorithm and used it to search through all the
  possible game-choice options and choose the best
  one. Combining all of these aspects together
  yielded a fully enjoyable and informative senior
  research project.

73
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• 0.2 Introduction and Background
• I am pleased to introduce Paintball Frenzy in all
  its glory. Originally, I had a massively
  ambitious pro ject proposal to design a computer
  programming language designed specifically for
  the creating of video games. I was quick to
  dispatch this proposal towards the end of my
  junior year. Also in my junior year I took the
  Artificial Intelligence class and I learned of my
  deep interest in the Artificial Intelligence
  field and the difficult minimax algorithm in
  particular (I was never able to master it in the
  class).

74
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• Therefore, I was quick to consider the minimax
  algorithm for my final pro ject. But I wanted to
  do more than just Artificial Intelligence, I also
  wanted to design my own game. So I did, and
  Paintball Frenzy was born. Background on game
  design.
• Background on graphics.
• Background on game AI.
• 0.3Theory
• My research was a three-pronged proccess. First,
  I chose to formulate an innovative turn-based
  game.

75
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• Game Design Theory. 1 Graphics Theory
• Intitially, I delved into my artificial research
  with impressive haste. I acquired a green
  artificial intelligence book from Mr. Torbert and
  I've been researching the minimax algorithm ever
  since. The turn-based, deterministic style of my
  program makes the minimax algorithm ideal.
  However, the multiplayer asect of the game will
  greatly slow down the algorithm and it'll most
  likely force it to use a cut-off test before the
  entire tree has been searched. This means,
  unfortunately, that at the faster time limits, my
  AI agent won't operate optimally.

76
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• More AI Theory. 0.4
• Design Criteria
• The progress of my pro ject is three-pronged. In
  the first quarter, I researched game design and
  programmed a working version of Paintball Frenzy
  withoutgraphics or an AI Agent. I used C to
  program. My Main function first initializes
  global variables like the board matrix. It then
  calls functions to display the title and generate
  the menus. After that, Main runs a do-while loop
  until the number of turns has expired. In this
  loop, Main calls functions to display the board
  and prompt the current player's move. Then Main
  repeats the loop with the next player,
  incrementing the turn counter as required.

77
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• After the loop, Main calls a function to evaluate
  and display the results of the game. In the
  second quarter, I used OpenGL graphics to make my
  program more aesthetically pleasing. I needed to
  make several design modifications to allow for a
  graphical interface. My main function is only
  responsible for initilizing variables and
  initiating the OpenGL display cycle. My display
  function (RunGame) regulates the game loop with a
  series of if else statements that assess the
  current game progress via mouse clicking history.

78
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• In my main game loop, I designed to have a second
  set of if statements to track the state of each
  particular turn. First, I start a timer, then I
  get mouse input until the timer has expired. At
  the end of the time, I excecute the players' move
  and signal the change in player or turn. I store
  the mouse clicking history by saving phases of
  the game as integers in a global variable. I also
  designed DispBoard to display the gameboard in
  the left part of window. The upper right side
  displays the current turn and player.

79
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• Below that is directional pad that records mouse
  input. I created three auxilary functions to
  handle mouse clicking, the mouse menu, and text
  output. I also designed the menu screen layout
  that would allow for alterable data and buttons.
  The second semester is consumed with my attempts
  to add an AI agent to my program. This includes
  the research of Game AI and more specifically the
  minimax algorithm. Techniques such as alpha-beta
  pruning where also researched. In the end, an
  optimized AI Agent was produced. I've redesigned
  my RunGame function to call the AgentMove
  function at the start of its time limit to allow
  for the most time.

80
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• I designed the first version of a recursive
  minimax function that I call maximax. My
  AgentMove function now simply sets some variables
  and calls maximax to get the new board position.
  First, maximax tests the turn limit and time
  limit constraits. If the time is up or the
  simulated game is over, the program calls the
  terminal function entitled EvalFunc. Next, I
  increment the simulated player or turn to delve
  further into the maximax tree. Then, I determine
  if the four directional moves are possible, and
  recurse in each of the legal directions. Then, I
  decide which one is the best move, and return the
  changed board. I also programmed the EvalFunc.

81
Paintball Frenzy Graphical Turn-Based Game With
a Minimax AI AgentChristopher Goss

• 0.5 References
• (this is merely a reference list right now, not a
  bibliography) http//www.latex-pro ject.org/
  http//www.vancouver.wsu.edu/fac/peabody/game-book
  /Coverpage.html http//www.msoe.edu/eecs/cese/reso
  urces/stl/string.htm http//www.tjhsst.edu/
  rlatimer/ http//www.opengl.org
  http//www.tjhsst.edu/ rlatimer/assignments2004/in
  dependenceday.txt The AI book http//ai-depot.com/
  LogicGames/MiniMax.html

82
Modeling Evolutionary BehaviorThe purpose of
this project is to attempt to model evolutionary
behavior in agents in an environment by
introducing traits and characteristics that
change with the different generations of
agents.I hope to create an environment where
certain agents will prosper and reproduce while
others will have traits that negatively affect
their performance. In the end, a single basic
agent will evolve into numerous subspecies of the
original agent and demonstrate evolutionary
behavior.
82
83
Modeling Evolving Social BehaviorStephen J.
Hilber

• 1 Abstract
• With the creation of Epstein and Axtell's
  Sugarscape environment, increasing emphasis has
  been placed on the creation of "root" agents -
  agents that can each independently act and
  interact to establish patterns identifiable in
  our everyday world. Models created for traffic
  patterns and flocking patterns confirm that these
  conditions are caused by each participating agent
  trying to achieve the best possible outcome for
  itself.

84
Modeling Evolving Social BehaviorStephen J.
Hilber

• The purpose of this project is to attempt to
  model evolutionary behavior in agents in an
  envionment by introducing traits and
  characteristics that change with the different
  generations of agents. Using the modeling package
  MASON programmed in Java, I will be able to
  create an environment where agents will pass down
  their genetic traits through different
  generations. By adding certain behavioral traits
  and a common resource to the agents, I hope to
  create an environment where certain agents will
  prosper and reproduce while others will have
  traits that negatively affect their performance.

85
Modeling Evolving Social BehaviorStephen J.
Hilber

• In the end, a single basic agent will evolve into
  numerous subspecies of the original agent and
  demonstrate evolutionary behavior. This pro ject
  will show that agents which possess the
  capability to change will change to better fit
  their environment.
• 2.1 Body Matter Introduction
• Computer modeling, simply defined, is the process
  of programming the conditions of an environment
  into a computer and adjusting parameters of the
  model to see how they affect the results of the
  model. Today, though, many computer models are
  merely used to verify behavior that we already
  suspect is accurate. Many models created today
  focus on topics such as disease, population
  growth, and traffic, where the results of the
  models were already well known.

86
Modeling Evolving Social BehaviorStephen J.
Hilber

• The models are used primarily to see how adding,
  subtracting, or otherwise changing any parameters
  in the environment affect the outcome of the
  groups in the environment or the environment as a
  whole. This opens the doors for many scientists
  and researchers to simple, cost-efficient
  experimentation. For example, analyzing traffic
  patterns requires a large committment to
  observation and analysis over months and even
  years. Such research is not undertaken lightly.
  With computer modeling, this information can be
  programmed into a model and used to estimate how
  the real-world system will react to changes in
  its stimuli.

87
Modeling Evolving Social BehaviorStephen J.
Hilber

• Also, computer modeling allows us to perform
  "experiments" in areas of science where
  experimentation is not normally possible. This is
  especially true in astronomy, where the sheer
  vastness of space and our relative insignificance
  to the universe means that experimentation is
  simply not possible. You can't reset the universe
  and watch over five billion years of history.
  However, computer models of planetary orbits and
  systems allows us to try and find explanations
  for phenonoma we have observed.

88
Modeling Evolving Social BehaviorStephen J.
Hilber

• In short, the majority of computer modeling is
  used to verify existing theories. Social
  behavioral patterns, although well-researched in
  general, have thus far been used to demonstrate
  how one individual interacts with his social
  environment as a whole. This pro ject, based off
  of such personality research pro jects as the Dr.
  John A. Johnson's IPIP-NEO and using evolutionary
  behaviors first established in Epstein and
  Axtell's famous Sugarscape models, attempts to
  use computer modeling as a form of research in
  and of itself into the evolutionary aspects of
  social behavior.

89
Modeling Evolving Social BehaviorStephen J.
Hilber

• In particular, my pro ject attempts to analyze
  the behavioral patterns of introverted agents and
  extroverted agents over a long period of time.
  With any luck, insights will be made into this
  exciting, riveting, and brilliant field of the
  human psyche.
• 2.2 Background
• Conway's Game of Life was the first prominent
  agent-based model. Each cell was an "agent" that
  contained either a 0 or a 1 (alive or dead)
  depending on how many neighbors it had, and acted
  independently of the environment. Conway's Game
  of Life didn't lead to any profound insights, but
  it did pave the way for future agent-based
  modeling.

90
Modeling Evolving Social BehaviorStephen J.
Hilber

• The advantage of agent-based modeling, as many
  found out, is that it did not assume prior
  conditions. It was a method of building worlds
  "form the bottom up", where independent agents
  were able to create complex worlds without any
  overseers. One popular psychological game,
  Prisoner's Dilemma, spawned a series of games
  where agents tried to maximize their outcome,
  often at the expense of other agents. Eventually,
  these agent-based models were incorporated in
  studies of flocking. The models created to show
  flocking behavior in birds did not incorporate
  flock leaders, as many presumed. Instead, the
  birds all acted for their own best interests, and
  directions and resting points were chosen as
  compromises of sorts.

91
Modeling Evolving Social BehaviorStephen J.
Hilber

• Using the theory that independent agents can
  create organized structures such as flocks,
  Epstein and Axtell created the Sugarscape world
  in an effort to discover if social behaviors and
  human characteristics could emerge through
  independent actions. The Sugarscape model had
  agents able to breed, fight, trade, and die, and
  the core of the model was the resource sugar.
  Each agent had a metabolism rate which burned off
  its sugar if it ran out of sugar, the agent
  died. Instead of isolated behavior, however, the
  agents soon used their resources to work
  together.