AntsFarming.nlogo

globals [
  food-collected
]

patches-own [
  chemical             ;; amount of chemical on this patch
  food                 ;; amount of food on this patch (0, 1, or 2)
  nest?                ;; true on nest patches, false elsewhere
  nest-scent           ;; number that is higher closer to the nest
  food-source-number   ;; number (1, 2, or 3) to identify the food sources
  is-grass?            ;; true if patch is grass
  remaining-grow-time  ;; number of ticks before food growing
  alive-time           ;; number of ticks food has been alive
]

turtles-own [
  time-carrying        ;; time required to drop
]

;;;;;;;;;;;;;;;;;;;;;;;;
;;; Setup procedures ;;;
;;;;;;;;;;;;;;;;;;;;;;;;

to setup
  clear-all
  ;;import-world "world.csv"
  import-pcolors "world.png"
  recalculate-world
  set food-collected 0
  set-default-shape turtles "bug"
  crt population
  [ set size 2         ;; easier to see
    set color red      ;; red = not carrying food
    set time-carrying 0 ]   
  ;;setup-patches-random
  reset-ticks
end

to recalculate-world
  ask patches [
    set nest? false
    set is-grass? false
    set food 0
    if pcolor = violet [ set nest? true ]
    if pcolor = green [ set is-grass? true ]
    if pcolor = 106 [ 
      set food 1
      set is-grass? true 
    ]
    setup-nest
  ]
end

to setup-patches-random
  setup-green-lands
  ask patches
  [
    setup-nest
    setup-food
    recolor-patch 
  ]
end

to setup-green-lands
  ask patches [
   if distancexy 0 0 > 10 [
     if random 100 < 25
     [
       set is-grass? true
     ] 
   ]
  ]
  ask patches [
   ifelse count neighbors with [is-grass? = true] > 2
   [ set is-grass? true
     set remaining-grow-time (random 50) + grow-time ]
   [ set is-grass? false ]
  ]
end

to setup-nest  ;; patch procedure
  ;; set nest? variable to true inside the nest, false elsewhere
  set nest? (distancexy 0 0) < 5
  ;; spread a nest-scent over the whole world -- stronger near the nest
  set nest-scent 200 - distancexy 0 0
end

to setup-food  ;; patch procedure
  ;; setup food source one on the right
  if (distancexy (0.6 * max-pxcor) 0) < 5 and is-grass? = true
  [ set food-source-number 1
    ;;set food 1
    ]
  ;; setup food source two on the lower-left
  if (distancexy (-0.6 * max-pxcor) (-0.6 * max-pycor)) < 5 and is-grass? = true
  [ set food-source-number 2
    ;;set food 2 
    ]
  ;; setup food source three on the upper-left
  if (distancexy (-0.8 * max-pxcor) (0.8 * max-pycor)) < 5 and is-grass? = true
  [ set food-source-number 3
    set food 3 ]
end

to recolor-patch  ;; patch procedure
  ;; give color to nest and food sources
  ifelse nest?
  [ set pcolor violet ]
  [ ifelse food > 0
    [ set pcolor scale-color blue food 0 5]
    ;; scale color to show chemical concentration
    [ set pcolor scale-color red chemical 0.1 8
      if is-grass?
      [ set pcolor green ] ] ]
end

;;;;;;;;;;;;;;;;;;;;;
;;; Go procedures ;;;
;;;;;;;;;;;;;;;;;;;;;

to go  ;; forever button
  ask turtles
  [ if who >= ticks [ stop ] ;; delay initial departure
    ifelse color = red
    [ look-for-food  ]       ;; not carrying food? look for it
    [ ifelse color = yellow 
      [ plant-food-resource ;; planting
      ]
      [ return-to-nest ] ]       ;; carrying food? take it back to nest
    wiggle
    fd 1 ]

  diffuse chemical (diffusion-rate / 100)

  ask patches
  [ grow-food
    die-food
    set chemical chemical * (100 - evaporation-rate) / 100  ;; slowly evaporate chemical
    recolor-patch ]

  if count patches with [food > 0] = 0 and count turtles with [color = red] = count turtles
  [ stop ]
  tick
end

to plant-food-resource
 set time-carrying time-carrying + 1
 if food = 0 and is-grass? = true and time-carrying > time-to-drop
 [
  set food 1
  set alive-time 0
  set color red
  set remaining-grow-time (random 50) + grow-time
 ] 
 if time-carrying > time-to-drop + 20
 [
   set color red 
 ]
end

to grow-food
  if food > 0 
  [
    ifelse remaining-grow-time > 0
    [
      set remaining-grow-time remaining-grow-time - 1
    ]
    [
      if food < 3 
      [ set food food + 1 ]
      set remaining-grow-time (random 50) + grow-time
     if count neighbors with [ is-grass? = true and food = 0 ] > 0
     [
       ask one-of neighbors with [ is-grass? = true and food = 0 ]
       [
         set food 1
         set alive-time 0
         set remaining-grow-time (random 50) + grow-time
       ]
     ]
    ]
  ]
end

to die-food
  set alive-time alive-time + 1
  if alive-time > grow-time * 5
  [
    set food 0 
  ]
end

to return-to-nest  ;; turtle procedure
  ifelse nest?
  [ ;; drop food and head out again
    set color red
    set food-collected food-collected + 1
    rt 180 ]
  [ set chemical chemical + 60  ;; drop some chemical
    uphill-nest-scent ]         ;; head toward the greatest value of nest-scent
end

to look-for-food  ;; turtle procedure
  if food > 0
  [
   ifelse random 100 < farming-probability [
     set food food - 1
     set time-carrying 0
     set color yellow
     stop
   ]
   [
     set food food - 1
     set color orange
     stop
   ]
  ]
;; go in the direction where the chemical smell is strongest
  if (chemical >= 0.05) and (chemical < 2)
  [ uphill-chemical ]
end

;; sniff left and right, and go where the strongest smell is
to uphill-chemical  ;; turtle procedure
  let scent-ahead chemical-scent-at-angle   0
  let scent-right chemical-scent-at-angle  45
  let scent-left  chemical-scent-at-angle -45
  if (scent-right > scent-ahead) or (scent-left > scent-ahead)
  [ ifelse scent-right > scent-left
    [ rt 45 ]
    [ lt 45 ] ]
end

;; sniff left and right, and go where the strongest smell is
to uphill-nest-scent  ;; turtle procedure
  let scent-ahead nest-scent-at-angle   0
  let scent-right nest-scent-at-angle  45
  let scent-left  nest-scent-at-angle -45
  if (scent-right > scent-ahead) or (scent-left > scent-ahead)
  [ ifelse scent-right > scent-left
    [ rt 45 ]
    [ lt 45 ] ]
end

to wiggle  ;; turtle procedure
  rt random 40
  lt random 40
  if not can-move? 1 [ rt 180 ]
end

to-report nest-scent-at-angle [angle]
  let p patch-right-and-ahead angle 1
  if p = nobody [ report 0 ]
  report [nest-scent] of p
end

to-report chemical-scent-at-angle [angle]
  let p patch-right-and-ahead angle 1
  if p = nobody [ report 0 ]
  report [chemical] of p
end


; Copyright 1997 Uri Wilensky.
; See Info tab for full copyright and license.
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@#$#@#$#@
## WHAT IS IT?

In this project, a colony of ants forages for food. Though each ant follows a set of simple rules, the colony as a whole acts in a sophisticated way.

## HOW IT WORKS

When an ant finds a piece of food, it carries the food back to the nest, dropping a chemical as it moves. When other ants "sniff" the chemical, they follow the chemical toward the food. As more ants carry food to the nest, they reinforce the chemical trail.

## HOW TO USE IT

Click the SETUP button to set up the ant nest (in violet, at center) and three piles of food. Click the GO button to start the simulation. The chemical is shown in a green-to-white gradient.

The EVAPORATION-RATE slider controls the evaporation rate of the chemical. The DIFFUSION-RATE slider controls the diffusion rate of the chemical.

If you want to change the number of ants, move the POPULATION slider before pressing SETUP.

## THINGS TO NOTICE

The ant colony generally exploits the food source in order, starting with the food closest to the nest, and finishing with the food most distant from the nest. It is more difficult for the ants to form a stable trail to the more distant food, since the chemical trail has more time to evaporate and diffuse before being reinforced.

Once the colony finishes collecting the closest food, the chemical trail to that food naturally disappears, freeing up ants to help collect the other food sources. The more distant food sources require a larger "critical number" of ants to form a stable trail.

The consumption of the food is shown in a plot.  The line colors in the plot match the colors of the food piles.

## EXTENDING THE MODEL

Try different placements for the food sources. What happens if two food sources are equidistant from the nest? When that happens in the real world, ant colonies typically exploit one source then the other (not at the same time).

In this project, the ants use a "trick" to find their way back to the nest: they follow the "nest scent." Real ants use a variety of different approaches to find their way back to the nest. Try to implement some alternative strategies.

The ants only respond to chemical levels between 0.05 and 2.  The lower limit is used so the ants aren't infinitely sensitive.  Try removing the upper limit.  What happens?  Why?

In the `uphill-chemical` procedure, the ant "follows the gradient" of the chemical. That is, it "sniffs" in three directions, then turns in the direction where the chemical is strongest. You might want to try variants of the `uphill-chemical` procedure, changing the number and placement of "ant sniffs."

## NETLOGO FEATURES

The built-in `diffuse` primitive lets us diffuse the chemical easily without complicated code.

The primitive `patch-right-and-ahead` is used to make the ants smell in different directions without actually turning.


## HOW TO CITE

If you mention this model in a publication, we ask that you include these citations for the model itself and for the NetLogo software:

* Wilensky, U. (1997).  NetLogo Ants model.  http://ccl.northwestern.edu/netlogo/models/Ants.  Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
* Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

## COPYRIGHT AND LICENSE

Copyright 1997 Uri Wilensky.

![CC BY-NC-SA 3.0](http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.  To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at uri@northwestern.edu.

This model was created as part of the project: CONNECTED MATHEMATICS: MAKING SENSE OF COMPLEX PHENOMENA THROUGH BUILDING OBJECT-BASED PARALLEL MODELS (OBPML).  The project gratefully acknowledges the support of the National Science Foundation (Applications of Advanced Technologies Program) -- grant numbers RED #9552950 and REC #9632612.

This model was developed at the MIT Media Lab using CM StarLogo.  See Resnick, M. (1994) "Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds."  Cambridge, MA: MIT Press.  Adapted to StarLogoT, 1997, as part of the Connected Mathematics Project.

This model was converted to NetLogo as part of the projects: PARTICIPATORY SIMULATIONS: NETWORK-BASED DESIGN FOR SYSTEMS LEARNING IN CLASSROOMS and/or INTEGRATED SIMULATION AND MODELING ENVIRONMENT. The project gratefully acknowledges the support of the National Science Foundation (REPP & ROLE programs) -- grant numbers REC #9814682 and REC-0126227. Converted from StarLogoT to NetLogo, 1998.
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<experiments>
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    <setup>setup</setup>
    <go>go</go>
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    <metric>food-collected</metric>
    <metric>sum [food] of patches with [ pcolor != green and pcolor != black ]</metric>
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    <enumeratedValueSet variable="evaporation-rate">
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    </enumeratedValueSet>
    <enumeratedValueSet variable="population">
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    </enumeratedValueSet>
    <enumeratedValueSet variable="diffusion-rate">
      <value value="23"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="grow-time">
      <value value="150"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="farming-probability">
      <value value="25"/>
      <value value="30"/>
      <value value="35"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
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