1. Evolving Cutting Horse and Sheepdog Behavior on a Simulated Flock Chris Beacham

2. Abstract The focus of this project is attempting to evolve the behavior of a single agent or small group of agents so that they can effectively direct the movement of a much larger group which displays flocking behavior. This is the same role sheepdogs play to manage herds of livestock.

3. Other Research There has been little development in this specific field. A lot has been focused on flocking, but not much on controlling flock movement. There was an attempt to build a robotic sheepdog, but their wasn’t much progress on the sheepdog’s behavior. It was an important proof of concept that a robot could be used to handle livestock.

4. Uses This project could have two foreseen applications: Animation – the current method of controlling flock direction for animation is quite crude. An invisible sheepdog might be very useful. Enhanced control of livestock – This is more unlikely. It’s possible that this project could yield new techniques for controlling flocks that are applicable to real life.

5. Sheepdog and Cutting Horse The sheepdog is used to direct the movements of sheep, ducks, cows, goats and other herd stock. It accomplishes this by moving around the herd or flock in specific ways. The cutting horse is used with herds of cattle. It’s job is to single out a single cow, and “cut” it off from the herd, thus allowing people to access it.

6. Flocking The herd will be simulated with a flocking algorithm. Three algorithms make up flocking behavior: Separation Cohesion Alignment

7. First Quarter The first quarter was spent creating a flocking behavior simulation, and debugging it. This is a screenshot of a flock flying to the right.

8. Second Quarter The first half of second quarter was spent getting the flocking simulation to settle down, and resemble a herd. This was finally accomplished, through lots of troubleshooting

9. Second Quarter After the herd was stabilized, a sheepdog agent was added to the simulation At this point, it’s behavior is fairly simple.

10. Second Quarter After trying out several methods of coding behavior, it was decided that the only one that could be implemented in a reasonable timescale was a weighting system Each sheepdog agent would add weight to pre- calculated vectors to determine it’s behavior. This doesn’t offer as much hope for dynamic behavior, but it is based off similar principals that flocking behavior is. If a good behavior is found, it will be easier to recreate and implement.

11. Second Quarter This is a screenshot of the first generation of sheepdogs. Their behavior is randomized, and incredibly chaotic. Unfortunately it is not visible in the still photo.

12. First Generation This is a sample of the genetic pool of the first generation. [32.164006627541823, -19.261148559544093, 28.838154710926517, -2.6177442818239252, - 1.610540774960918, -3.0347756036751052, -20.931229810205998, 5.7627079638362986, 1.7473837471501998, 7.7198793790136833], [-11.470881676551826, 28.336696618106188, 38.987209868269673, 2.4890967130213557, - 0.078896899902183293, 4.0279192032567082, 38.113753797435876, -9.9091943197785568, 6.0767013600522422, 22.90210747684651], [19.524480722525141, 17.865021602410764, 35.768937982290353, 2.5848421326612847, 1.7679235149952088, 1.780983430333368, 35.344660445143845, 8.9854097668224071, 0.58138734205464693, 19.580526983833721], [14.652628528856667, 37.731823468942835, 16.911577862190441, 0.4858339804315932, 4.2518008367774591, -4.8959446134369822, -12.527178547904271, -23.834582722704489, 2.5735304563845185, 10.983380384038441], [-37.365308224600689, -26.219646166636444, 23.837388022611961, -2.6502990318154742, 2.5285789988460721, -1.9174224730964582, -4.8617111048341215, -27.040194863370225, 5.3442590210258247, 47.707995016754595], [-47.83280830026532, -27.746753711795826, -22.465210886399479, -1.8834401853055285, - 4.6526740112824712, 2.1138389668398263, 4.7442060317506503, 13.681368603073729, 1.7036710633058849, -24.335868677880825], [31.332197286921499, 37.788533831872257, -45.919434849424789, 0.48534266655833669, - 3.3026128229853424, -1.7377195224251816, 7.2576919556413912, -36.199633586541836, 4.8382490300740457, 4.0074023699981822], [3.2210497987682132, 10.765683943559381, -24.469857259276651, -1.9355065069590394, - 0.61633090971086801, -3.1878785380980879, 41.904929197287117, 3.5243513927757348, 5.4548537085994404, 12.247444295078616],

13. Precursors Initially, an evolution program was written to fine tune the authors understanding of artificial evolution. This is a genepool of evolved creatures.

14. Programming Tools Python is being used for the code, with tKinter providing the graphics. Breve was looked at as a possibility, but it was dismissed for being to complex and not documented.