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; Exercise
; Set population to 200, and diffusion rate to 20.
; For each evaporation-rate value below, run the model five times, and for each run, record the number of ticks it takes until all the food is eaten.
; Then average these five numbers; this is the "average time taken" for this value of evaporation-rate.
; Which value of evaporation-rate below makes the ants quickest (on average) to each all the food?
; a. evaporation-rate = 0
; trial 1: 580 ticks
; trial 2: 727 ticks
; trial 3: 630 ticks
; trial 4: 1035 ticks
; trial 5: 768 ticks
; average: 748 ticks
; b. evaporation-rate = 5
; trial 1: 467 ticks
; trial 2: 808 ticks
; trial 3: 510 ticks
; trial 4: 492 ticks
; trial 5: 808 ticks
; average: 617 ticks
; c. evaporation-rate = 20
; trial 1: 1642 ticks
; trial 2: 1405 ticks
; trial 3: 1514 ticks
; trial 4: 1605 ticks
; trial 5: 1721 ticks
; average: 1
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
]
;;;;;;;;;;;;;;;;;;;;;;;;
;;; Setup procedures ;;;
;;;;;;;;;;;;;;;;;;;;;;;;
to setup
clear-all
set-default-shape turtles "bug"
crt population
[ set size 2 ;; easier to see
set color red ] ;; red = not carrying food
setup-patches
reset-ticks
end
to setup-patches
ask patches
[ setup-nest
setup-food
recolor-patch ]
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
[ set food-source-number 1 ]
;; setup food source two on the lower-left
if (distancexy (-0.6 * max-pxcor) (-0.6 * max-pycor)) < 5
[ set food-source-number 2 ]
;; setup food source three on the upper-left
if (distancexy (-0.8 * max-pxcor) (0.8 * max-pycor)) < 5
[ set food-source-number 3 ]
;; set "food" at sources to either 1 or 2, randomly
if food-source-number > 0
[ set food one-of [1 2] ]
end
to recolor-patch ;; patch procedure
;; give color to nest and food sources
ifelse nest?
[ set pcolor violet ]
[ ifelse food > 0
[ if food-source-number = 1 [ set pcolor cyan ]
if food-source-number = 2 [ set pcolor sky ]
if food-source-number = 3 [ set pcolor blue ] ]
;; scale color to show chemical concentration
[ set pcolor scale-color green chemical 0.1 5 ] ]
end
;;;;;;;;;;;;;;;;;;;;;
;;; Go procedures ;;;
;;;;;;;;;;;;;;;;;;;;;
to go ;; forever button
while [any? patches with [food > 0]] [
ask turtles
[ if who >= ticks [ stop ] ;; delay initial departure
ifelse color = red
[ look-for-food ] ;; not carrying food? look for it
[ return-to-nest ] ;; carrying food? take it back to nest
wiggle
fd 1 ]
diffuse chemical (diffusion-rate / 100)
ask patches
[ set chemical chemical * (100 - evaporation-rate) / 100 ;; slowly evaporate chemical
recolor-patch ]
tick
]
end
to return-to-nest ;; turtle procedure
ifelse nest?
[ ;; drop food and head out again
set color red
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
[ set color orange + 1 ;; pick up food
set food food - 1 ;; and reduce the food source
rt 180 ;; and turn around
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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"" ""
PENS
"food-in-pile1" 1.0 0 -11221820 true "" "plotxy ticks sum [food] of patches with [pcolor = cyan]"
"food-in-pile2" 1.0 0 -13791810 true "" "plotxy ticks sum [food] of patches with [pcolor = sky]"
"food-in-pile3" 1.0 0 -13345367 true "" "plotxy ticks sum [food] of patches with [pcolor = blue]"
@#$#@#$#@
## WHAT IS IT?
(Note: This is a slight modification of the original Ants model from the NetLogo Models Library: this version of the model stops once all the food has been eaten.)
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 [email protected].
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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