Emergent Behaviour and Artificial Life
Dan Taylor (firstname.lastname@example.org)
After several days of wracking my brains over what I should talk about at the September UKBUG meeting I decided
upon "Something to do with A-life". A-Life has always been a topic that's interested me and it's great fun
to play with. It's not very serious though, or, for that matter, very practical. But A-life systems do demonstrate an
important characteristic: Emergent Behaviour.
Due to time considerations I haven't had chance to write a very serious article to accompany the talk, but I have put
together this article, which is more of a journal, documenting the design and implementation of the demo programs
Animal Kingdom and Animal Deliveries. The contents of this article are, more or less,
a transcription of the posts I made on the
Logical Genetics Forums
during the two weeks leading up to the talk.
Animal Kingdom and Animal Deliveries are based on a program that I wrote for a second year
project at university with a guy called Nick Johnson. That program was called Donkey World and consisted
of a flat square world of small square tiles. The tiles all had a certain amount of grass on them, which grew as time went
by. Donkeys were allowed to roam the world randomly, eating the grass as they went. Donkeys had no idea about the shape
of the world, or in fact of anything other than the tile they were standing on and the eight tiles around them. They
were able to reproduce if they found a suitable mate and able to eat grass to survive. Despite the fact that the donkeys
only had a very simple rule based "intelligence" they still formed groups and created a sustained population.
The aim of Animal Kingdom is to develop a more complex system where different types of animal
interact in a similar world.
The first task is to develop a class heirarchy for animals and another for the map. The first screen shot, shown in figure 1,
shows the map
view. The map view is based on a TDrawGrid. The OnDrawCell event has been overridden to allow various bitmap files to be
Figure 1: The map
Note that I've added water squares round the edge of the map. This means that land animals can't walk off the edge of the
Figure 2 shows a similar map (with a view of another part of the coastline). I have now populated the map with a set of
creatures. The pink ones are girls and the blue ones are boys (obviously). The creatures are of class TRabbit, but
I haven't got round to drawing a rabbit bitmap yet!
Figure 2: The "rabbit" population
I have also implemented a details dialog, so you can see the creature details.
Figure 3: The details dialog is displayed when a tile is double clicked
The next step is to get the rabbits to move around, eat and bonk.
That just means adding a few routines to allow animals to detect food and other animals in their
8-neighbourhood (i.e. the eight squares around them), decide whether they are fit for a bonk and
then initiate the bonk itself. Once a bonk has been successful (which is based on a probability)
the females are pregnant for a number of turns before giving birth. Only creatures of 16 turns old or
older will be allowed to bonk. This is not just for the sake of decency, it's to stop male babies
immediately impegnating their mothers after they are born!
Figure 4 shows the population after a birth explosion.
Figure 4: Population explosion leads to a lack of food and death for the rabbits
With the original Donkey World we managed to get the population to reach an equilibrium - albeit a sinusoidal one.
The important thing is to get the eating and grass growing rates to even out so that the population doesn't crash to nothing
aftwer the rabbits eat all the grass.
Figure 5 shows another screen shot with a graph to show the population size of male and female rabbits over time.
Figure 5: The new population graph, showing populations of male and female rabbits
At this point in development I noticed a bug which caused all the rabbits to move to the top corner of the map. This was because
of the way the "RandomTile" method was implemented. "RandomTile" is supposed to return a reference to the first tile in the
animal's 8-neighbourhood on which the animal is able to survive. The method itterated through the neighbouring tiles
clockwise from north. This meant that habitable tiles in a general north-eastery direction were favoured. In order to
compensate for this it was necessary to itterate through the directions at random. A friend of mine suggested the following
technique (listing 1) which seemed to work well.
function TAnimal.RandomTile: TTile;
i, j, Temp : Integer;
Dirs : array [0..7] of Integer;
DirAllowed : array [0..7] of Boolean;
for i := 0 to 7 do
DirAllowed[i] := True;
for i := 0 to 7 do
j := Random(8);
while DirAllowed[j] = false do
j := (j + 1) mod 8;
Dirs[i] := j;
DirAllowed[j] := False;
for i := 0 to 7 do
if Assigned(Tile.Neighbours[Dirs[i]]) and
Tile.Neighbours[Dirs[i]].Col) = nil) then
Result := Tile.Neighbours[Dirs[i]];
Result := nil;
Listing 1: Code to randomly select a tile to move to
I managed to get the population to stabilise as soon as the random movement method was fixed.
Figure 6 shows the graph that was produced after I left the simulation running for the time it takes to buy and eat a
flapjack from the food van.
Figure 6: A stable population of rabbits
Figure 7 is another screen shot. It shows the results of altering the rabbits class so that they behave in
the following way (listing 2)
if Hungry and FoodOnThisTile then
else if Hungry then
Listing 2: New rabbit behaviour
This is an alteration from the previous code (listing 3)
if Hungry and FoodOnThisTile then
Listing 3: Old rabbit behaviour
The goal seeking element of the new algorithm, which causes hungry bunnies to move to the tile with the most food,
allows bunnies to live longer. The goal seeking, combined with the random exploration which occurs when rabbits which
aren't hungry move to a random tile, causes the population to spread much more evenly across the map. A much larger
population can be sustained, with little or no fluctuations after the initial "baby boom". The rabbits eat almost all
the food on the map though, quickly finding and devouring any pockets of grass they find.
Figure 7: The rabbit population with the new behaviour
So now I have a working version of Donkey world. The next step is to develop some predator animals to
see if I can reproduce some interesting interactions between two species. Figure 8 shows a fox.
Figure 8: After the addition of foxes to the simulation
In first experiments, 20% of the initial population were foxes.
Foxes bonk and reproduce in much the same way as rabbits (exactly the same way in fact)
but, obviously enough, don't eat grass. Instead they eat rabbits.
Initially the fox population crashed almost immediately, as they moved randomly and
didn't actively seek either food or sex. Figure 9 shows the rise and fall of the fox
population. Note also the addition of the population "Mini Map" which shows the distribution of the
animals around the world.
Figure 9: The rise and fall of the fox population. Foxes in yellow/green
In order to try to extend the life of the fox population I implemented some goal seeking (for food and
sex) for the foxes. Getting the two populations to stabilise was a very hard problem indeed. In fact
I didn't manage to get a stable fox and rabbit simulation running at all. The problem is that foxes
are dependant on rabbits, but rabbits are not dependant on foxes, so the foxes are inherently the
weaker species. Figures 10 and 11 show the foxes slightly extended lifespan with the new heuristic
(goal seeking) behaviour. Unfortunately in these cases they wipe out the rabbit population and
their dependancy on the rabbits for food causes them all to die.
Figure 10: The fox population crashes due to a lack of food
Figure 11: Another crashing fox population
Figure 12 shows an animation of the Mini Map over time. White dots are rabbits and blue dots are
a new species of animal:
Hares. Hares are very similar to rabbits, but are more intelligent and faster, i.e. they goal seek
bonking opportunities and move two squares per cycle.
Figure 12: Animation of a population of rabbits and hares over time
At this point I also added new graphics for rabbits and hares. Hares are the ones with longer legs. Blue
creatures are male and pink creatures are female.
Figure 13: Hares and rabbits
I descovered early on that the hares "intelligence" allowed them to quickly dominate,
wiping out the rabbits by eating all the food and outliving the foxes.
Watch the animation in figure 14 to see the hares take over the world.
As far as I can tell, the reasons they do this are:
- They goal seek bonking, so they are able to cope with being widely dispersed accross a relatively empty map - unlike the rabbits
- They move faster than the rabbits, thus beating them to the food
- They do not rely on a high population of other creatures for survival, like the foxes do
Figure 14: Hare dominating in a population of all three species
Suprisingly, even after lowering the probability of a lady hare getting pregnant to just
10% they still seemed to dominate.
The Animal Kingdom simulation is interesting enough. It illustrates many emergent
behaviours among populations of simple interacting artificial organisms. It isn't a very "real world"
simulation though, and fails to demonstrate how we can design things using emergent behaviours.
This section details the other demo program I developed: Animal Deliveries. This
program illustrates how a complex task can be solved by a set of cooperating agents.
The problem is best described using the rough sketch from my lab book shown in figure 15.
Figure 15: The Animal Deliveries problem
Basically the animals are replaced by two types of robot, water robots (boats) and land robots (trucks).
The job of the robots is to deliver parcels from the left hand side of the world to the right.
Cooperation is required as there is a big river in the middle of the map. Boats can not sail on land
and trucks can not drive through water, but both can meet on a thin strip of beach on each side of
the river, where parcels must be exchanged.
The task in hand is to develop some very simple rules for both types of robot to get them to
deliver packages successfully. Figure 16 is a screen shot of the robots and the new tarmac world.
Figure 16: The Animal Deliveries world
Listing 4 shows the behaviour for the Boat and Truck agents. Both types of agent have the same
behaviour at the moment. If they are hungry (i.e. their batteries are running low) they head towards a
charging point. Otherwise they head left. When they reach the far left edge of the map they
randomly "search" for a parcel. When they reach a parcel they pick it up and head right. When they
can not travel any further to the right they drop the parcel and head back towards the left to find
another. The "Searching" boolean is an internal state variable which is used to control this behaviour.
Charger : IThing;
Parcel : IThing;
//References to anything interesting we might be stood on
Charger := Tile.ThingByType('TChargingStation');
Parcel := Tile.ThingByType('TParcel');
if Assigned(Charger) and not Carrying then
if IsHungry then
if Carrying then
else if Carrying then
if not CanSurviveOn(Tile.EastTile) then
else if Seeking and Assigned(Parcel) then
else if not Seeking then
if not CanSurviveOn(Tile.WestTile) then
Seeking := True
Listing 4: Behaviour of the boat and truck classes
Figure 17 shows the Animal Deliveries simulation running. Note that even though there is
no global control for the simulation and the robots are incapable of communicating and have no
understanding of the size and topology of the environment in which they live, they are still able to
cooperate to deliver the parcels to the right hand side of the map.
Green blobs are parcels, red blobs are trucks, light blue blobs are boats and the white blobs correspond to
charging stations - though these are not used properly yet.
Figure 17: The Animal Deliveries simulation running. Trucks are red, boats are blue and parcels are green
As you can see, the parcels appear in the left hand 6th of the world and are quickly
dispatched to the right hand side.
In order to do this I had to implement two new types of tile: Depot tiles, where
parcels can be dropped off and Beach tiles, which can be occupied by both boats and trucks
(in order to allow for parcel changeover).
Figure 18 shows a picture of trucks and boats exchanging parcels on the left hand beach:
Figure 18: Trucks and boats exchanging parcels on the left hand beach
Figure 19 is a picture of trucks dropping off parcels on the right hand side of the map:
Figure 19: Trucks dropping off parcels on the right hand side of the map
Feel free to download, play with, alter and extend the demo programs as you see fit.
If you manage to generate any interesting behaviours or would like to discuss the
software then feel free to get in touch via email or the
Logical Genetics Forums.
I'd be very interested to hear from anyone who has created more animal classes or demonstrated any
interesting new emergent behaviours.
A Zip archive of all the Delphi code for the project (3.06Mb)
"Emergent Behaviour and Artificial Life" (all text and figures) © 2003 Dan Taylor, Logical Genetics