Biologically Inspired
Computation
Lecture 5: Introducing Swarm Intelligence
contents: the behavior of natural swarms and
flocks -- Reynold’s rules and swarm
simulation.
Some of the images in this lecture come from slides
for a Course in Swarm Intelligence given at :
Swarms, and how they might inspire us
There are some interesting things that come to mind when we
think of swarms (flocks, schools, etc …):
• A swarm sometimes seems to behave as if it is an individual organism.
Ants or wasps on a hunt for food, or on the attack, behave as if with a single
mind, co-ordinating different actions with different parts of the swarm.
• A swarm, of ants/bees/locusts/etc often exhibits behaviours that seem clearly
more intelligent than any of the individual members of it.
• The way in which swarms in some species change direction is astoundingly
well co-ordinated.
• The way in which swarms in some species avoid obstacles seems to be
extremely well choreographed
Other puzzling things that swarms do
• Termites build huge nests – how?? Is an individual
termite clever enough to do this?
• Bees build hives, with complex internal structure
-- same question.
How on earth can these things happen?
HERE IS A LIVE EXAMPLE
Two simple rules: While continually wandering randomly:
If you are empty-handed and encounter a disc, pick it up
If you are carrying a disc and encounter another of the same colour,
put yours down.
Emergent order arises from simple local rules.
We sometimes exhibit swarm behaviour too
But we’re mainly interested in animals and insects
locusts
ants
Because we might
learn something
Why does flocking/swarming occur
so much in nature?
Energy savings: Geese in V formation have around a 70% greater
range than in flying individually. Individuals can fly around 25%
faster (why?).
Frightening and confusing predators; avoiding being “picked off”
Helping to catch prey: e.g. tuna school in a crescent shaped flock
with the concave part forward:
This is thought to help channel
their prey to the “focus”, and
stop them from escaping
It also maybe helps with migration
If we can assume that:
– An individual has an idea, but not a perfect one, of where to
go … e.g. by itself it may go a few degrees off course.
– The “errors” of individuals are not correlated (i.e. they’re all
wrong in a randomly different way)
– An emergent result of the flocking is that the flock’s direction
is the average of its members’ directions.
Then: basic statistics can show that the error in the flocks
direction is probably very small. About 1/sqrt(n) of
the typical error of one of the n individuals.
So …
Flocking occurs so much because it is clearly useful.
But how do they do it so well? Individual ants are
not clever enough to understand the benefits.
It comes down to: simple behaviours of individuals
in a group can have useful emergent properties.
A theme we will continue to see a lot …
Another kinds of swarm behaviour is the dynamics and
evolution of ideas as they get passed on and changed in
social networks.
A recommends to B, B recommends to C, …
The Adaptive Culture Model
Robert Axelrod has a well-known theory, “Axelrod’s Culture Model”,
which explains how ideas spread in societies. Kennedy and Eberhart
(a computer scientist and a social scientist respectively) altered this
into the “Adaptive Culture Model”, which works like this:
If you think your neighbour is good, then be more like them.
And that’s basically it. But notice the important words,
neighbour: you change yourself under the influence of people nearby
good:
in some way your neighbour is more optimal than you,
otherwise why be like them?
more like: this is vague, so you have freedom in how you change
This is actually a very good model for how culture and ideas
spread quickly in societies. Everything from rumours to eating
habits. I only hope this works with `green’ behaviour …
Back to computer science …
From the CS viewpoint, the question is:
How does this kind of , apparently organised, group behaviour
emerge, without a central controller? Without (like we have)
something like a brain in control of everything?
The emergent behaviour that we see arises purely as a result of
individuals in the swarm processing information in their (fairly)
immediate neighbourhood.
So, studying this in nature suggests how we can get co-ordinated
behaviour from a group of individuals, without having to specify
any overall controller. This is very useful, for example, for designing
computer networks. If one main machine was in control of the network,
and that machine crashed, …
But so far that has not been a main success area for swarm
inspiration …
Two main things that come from swarm
inspiration:
Optimisation algorithms.
Ants seem to find the shortest path to find food that may be
quite distant from their nest. They do this via “stigmergy” –
laying pheronomones on their path as they move. This has
directly inspired the design of a very successful optimisation
method.
Meanwhile, the adaptive culture model has led to a different,
and also very successful, new optimisation algorithm
Simulations of natural flocks.
For the entertainment and gaming industries, for example.
Craig Reynolds and “Boids”
Craig Reynolds is a computer graphics researcher, who revolutionised
animation in games and movies with his classic paper :
Reynolds, C. W. (1987) Flocks, Herds, and Schools: A Distributed Behavioral Model, in
Computer Graphics, 21(4) (SIGGRAPH '87 Conference Proceedings) pages 25-34.
This paper is examinable reading, available on my teaching page.
The story is:
• before this paper, animations of flocks, swarms, groups, and so on,
behaved nothing at all like the real thing. Nobody knew how to make
it realistic. (we still have that problem with fire, explosions, and
realistic human movement, etc …)
• Reynold’s solved the problem by trying a very simple approach,
which was inspired by a sensible view of how animals actually do it.
The problem
We would like these to move like a realistic flock of starlings.
(The heading of each one is suggested by where it’s pointing)
But what’s wrong to start with?
The problem
That’s better. Now what? Perhaps in the next timestep, they
should all move the same small distance? They should all change
their velocity in some way? What?
Reynold’s Rules
Reynolds came up with three simple rules that solve this
Problem, resulting in entirely realistic flocking behaviour.
To explain them, we first need to consider the perceptual system of
an individual (which Reynolds called a boid).
For realistic movement, you need a realistic view of perception.
E.g. a starling’s movement is not influenced at all by the flockmates
that it cannot see – such as those out of its line of sight, or too far
away.
A simple sensory system
This picture is from Reynold’s boids page.
The green boid can see a certain amount
ahead, and is also aware of any
flockmates within limits on
either side (recall, birds tend
to have eithers on the sides
of their heads.)
Two parameters, angle and distance,
define the system. SO, this boid will only
be influenced by those others it can sense
according to these parameters.
Rule 1: Separation
At each iteration, a boid
makes an adjustment to its
velocity according to the
following rule:
Avoid getting too close to
local (the ones it is aware
of) flockmates.
Rule 2: Alignment
At each iteration, a boid
makes an adjustment to
match its velocity to the
average of that of its local
flockmates.
Rule 3: Cohesion
At each iteration, a boid
makes an adjustment to its
velocity towards the
centroid of its flockmates.
Notes: It’s not quite as simple as that
to get realistic behaviour
Need to define an appropriate distance for the perceptive range.
What if this is too high, what if this is too small?
Reynold’s found that he had to be careful about how the vectors from
the three rules get combined. It is not ideal to simply add them.
Opposing “shouts” from two rules may cancel out, leading to
the third winning – in what scenarios might this be a problem?
Note that the cohesion rule is interesting – it leads to “bifurcating”
around obstacles – a follow-the-leader approach to flocking would not
achieve that.
The simple rules also realistically lead to “flash expansion” if started
too close together.
Next time:
Ant Colony Optimization