Download 6,9 October 2008 - School of Computing

Bioinspired Computing
Lecture 2
Collective Behaviour and
Swarm Intelligence
Nearly all slides …
• By Seth Bullock (2005 lectures)
Overview…
This Time:
• command & control vs. selforganisation
• the sophisticated abilities of
insect colonies
• stigmergy
• algorithms inspired by insect
intelligence
–
–
–
–
Foraging
Clustering & Sorting
Building
Swarming & Flocking
Next Time:
• insect-like social robots
– sorting & clustering
– cooperative transport
• Applications
– sport
– education
– entertainment
Amazonian Categorisation
• Imagine you are faced with the task of organising a huge number of
Amazonian species. There are hundreds of ways in which these
species might be divided up: colour, size, etc.
• Your boss tells you that your categorisation will have to
match that of his customers over a very long time scale, so
it must be flexible, because not only will customers change,
but species may also change (colour, size, prevalence, etc.).
• One approach to this knowledge management problem is
to interview customers, devise a general-purpose, explicit
categorisation scheme, pay someone to keep it up to date,
and hope customers and species change little and slowly.
• amazon.com solve an analogous problem like this:
“Customers who bought this book, also bought…”
Collective problem−solving
• "Problem−solving can occur at a level above a
collection of idealized agents, without
"intentional solving" on the part of the individual.”
–
N.L. Johnson, Collective Problem Solving, LANL tech−report, 1998.
• In other words, the individual agents do not
know they are solving a problem, but their
collective interaction so solves the problem.
• Emergent functionality
Amazonian Self-Organisation
Why is this automatic categorisation approach successful?
• no need to interview customers
• no need to discover explicit categories before using them
• adapts to changing trends automatically as they happen
In contrast, “command and control” approaches suffer from
the problem that explicit, hand-designed categorisations…
• may be hard to discover through customer interrogation
• will require constant updating and may still be out of date
• may sometimes require a radical overhaul
In the next few lectures, we will learn that simple, selforganising systems such as amazon.com’s often enjoy
advantages over their command and control cousins…
Advantages
These systems tend to involve many partially independent entities
working together to solve a problem without a central executive.
Each entity may be unaware of many or all of its colleagues,
attending to only its local environment.
Such systems are:
• parallel systems of simple
agents
• robust to noise & damage
• dynamic
• flexible
• self-organising
•
•
•
•
•
•
adaptive
possibly complex
hard to build?
hard to understand?
fast, cheap, out of control?
intelligent?
Ants, Termites, Bees, etc.
Some of the most impressive natural self-organising systems are to
be found in the world of colonial insects…
Such species…
• forage for food, dividing colonial resources effectively
• construct complex hives, nests, etc.
• efficiently & dynamically divide labour amongst the colony
• sort and cluster different objects (eggs, corpses, etc.)
• cooperate in moving objects, defeating enemies, etc. that
would be impossible for a single individual to deal with
…with no central planning and very little communication –
complicated, coordinated, goal-directed behaviour often
seems to arise spontaneously from the interactions of many
simple insects.
E.g.
Monte-Carlo simulation
Ant Algorithms and Bee Bots
Recently software engineers and roboticists have begun to exploit
our understanding of social insect behaviour to design new kinds of
algorithm and new kinds of robot.
• These systems idealise insect behaviour in much the same
way that ANNs idealise the behaviour of neurons (topic 3)
• Researchers pick and choose aspects of natural systems in
the hope that the artificial systems they inspire will share
some of their desirable properties.
• Of course, ants and bees are not designed to solve the
problems of today’s software engineers or roboticists.
• A piece of software or a robot will not perform well just
because it behaves like an ant colony – the trick is to find
aspects of insect behaviour which can be profitably
exploited.
Foraging for the Shortest Route
A particularly striking result from ant experimentation concerns the
ability of a colony to discover the shortest routes to the resources it
requires:
nest
As ants forage they deposit a trail of
slowly evaporating pheromone.
Those that reach the food first return
food before the others.
• One pheromone trail is now stronger than the other,
directing the ants to the food via the shorter route.
• It is not just ants that need to find optimal pathways.
• Traffic on telecommunications systems, the internet,
roads, rail, and sea would all benefit from the reduction in
congestion that efficient routing algorithms could provide.
Are larger pathways MSTs?
• Still to be investigated …
Ant-Inspired Routing
Consider an in-car system that suggests the best route to take for
any journey from A to B across a road network.
B
A
An “ant” traverses the network following the
strongest pheromone trails from A to B.
At the end of the journey the ant lays down
pheromone along the path taken, leaving less
pheromone at nodes that were congested.
In this way, routes via congested nodes are gradually
weakened, prompting ants to take alternative paths.
Since many ants traverse the network constantly and their
pheromone evaporates gradually, the system automatically
adapts to the current load on the road network.
How Good Is It?
• In their book Swarm Intelligence, Eric Bonabeau, Marco Dorigo &
Guy Théraulaz claim that “work on ant-based routing is only
beginning … but in all tested situations it appears that … ant-based
routing with agents patrolling the network outperforms all other
routing algorithms”.
• France Télécom and BT are developing algorithms for their
systems, but the application of ant-based routing is
potentially much wider – e.g., routing internet traffic.
• Like amazon.com, these algorithms rely on constant user
traffic to build an up-to-date picture of what is going on
(whether it be trends in book shopping, jams on the Otley
Road, or congestion at telecom hubs). The power of these
algorithms is their simplicity and their ability to direct traffic
and build this picture simultaneously.
Sorting, Clustering & Building
Many species of ant cluster corpses into cemeteries, gradually piling
them up together. Brood sorting is also observed, with larger larvae
lying further from the brood centre. In addition, some species are
able to construct walls, arches and other architectural structures.
These behaviours are yet to be fully understood, but have all
been modelled as the result of simple probabilistic rules:
•
Clustering relies on two rules concerning the ant’s local
environment:
(i) items are more likely to be picked up when they
differ from those around them, and
(ii) items are more likely to be put down amongst
similar items.
•
Wall building, etc., is slightly more complex, relying on
chemical templates to direct what are essentially the
same basic processes.
Clustering
Deneubourg (1998):
• Memory
•I N items, time T: f = N/T
Sorting
•Consider sorting in a high-dimensional space:
•Bank customers: age, income, occupation,..
•Colours: RGB values
•Introduce a 2D grid
•Define a neighbourhood s in which an agent has to look
•Insert objects on random places on the grid
•Define a distance between their attributes
•Modify local density f according to distance
Use slightly modified pick-up and drop-down probabilities:
Agent based
system
Starting form a
homogeneous
distribution
Ants for Catering
Imagine you’re want to seat many guests. It’s best if you group
guests that know each other together. But how?
• First draw a graph that represents which of your guests
know each other. Then apply an algorithm inspired by ant
clustering: scatter the nodes of the graph and a load of ants
on a page; let an ant pick up a node, i, if it is surrounded by
nodes to which i is not connected; let an ant drop i if it is
surrounded by graph neighbours of i; let the ants wander
about at random picking up and dropping nodes.
Slowly, clusters of acquainted guests will form on the page.
This graph-partitioning technique has applications in chip
design (where connected components must be placed close
together on a chip) and load balancing on parallel processor
machines.
Partitioning a Graph
Here we see a random graph being
partitioned by ants…
After the ant algorithm of
Kuntz, Layzell & Snyers (1997)
has been at work, a few clear
clusters have emerged.
Cluster members are more
connected to each other than
to members of other clusters.
This technique can be used to
efficiently load the processors
of a parallel machine –
minimising the amount of
communication required.
KLS -Algorithm
•
•
•
•
Sorting as before:
Modified distance concept
Let G be a graph, vi a node in the G
Let ρ(vi) be the set of adjacent vertices of vi
(including itself)
• Define d(vi, vj) = |D(ρ(vi) , ρ(vj))|/| ρ(vi) + ρ(vj)|
• D(A,B) = (A U B) – (A ∩ B)
Example of a routing algorithm
• Routing table:
– Each node i has a routing table
– k_i neighbours, N-1
destinations
– rik,s: neighbour k, destination s,
of node i
• Updating:
– Retain probabilities
• Deal with congestion:
– Introduce age: the older the ant
becomes, the less pheromone
its drops
– Age depends on spare
capacity of the node
Ants for Architecture
How can insects in a colony coordinate their behaviour in order to
build highly complex architectures? Ants and termites don’t appear to
have blueprints in their heads: they seem to follow simple rules in an
almost random manner.
If the blueprint isn’t in the insects’ heads, it may be in their
environment: ants appear to use their own previous work to
stimulate their behaviour – the building of arches, towers,
etc., appears to be governed by the structures themselves.
“The worker does not direct his work, but is guided by it”
Stigmergy
Stigmergy is a slippery concept. At its root is the ability of agents to
influence each other and their future selves by altering their
environment – often seemingly unknowingly.
Some examples:
• pedestrians crossing a park make paths in the grass – the
most popular will guide future walkers and be reinforced
• amazon.com customers buy books – their purchases change
the descriptions of the books, guiding future customers
• cells divide & differentiate during morphogenesis according
to chemical gradients that they themselves influence
In these examples, the behaviour of individual walkers,
shoppers, cells, etc., is shaped by their environment, which
in turn was shaped by their own prior activity.
Artificial Architects
Bonabeau and Théraulaz have demonstrated these ideas in action
through simple artificial paper wasp architects.
Wasps wander at random over a 3-d grid of cells and follow
a simple set of microrules that govern building behaviour.
Depending on the contents of the 26 cells that surround the
wasp, it can deposit one of two types of brick, or leave the
cell alone – thus wasps are reactive agents with no memory.
B&T show that starting from a single brick, swarms
of these wasps following simple sets of microrules
can construct complex structures that resemble
natural paper wasp nests – layered, cellular combs
?
with internal cavities and a surrounding envelope.
Could sets of these microrules be evolved to build human
habitation or useful artefacts?
- cf work on urban planning by Georg Vrachliotis, ETH Zurich
Termite Construction Simulation (D Ladley,
Leeds)
Building the queen chamber
3 kinds of termite:
-
trail-laying
Follow/lay pheromone
Nursing
Move towards/away
queen up to fixed
distance
Building
To place block:
Pheromone level must lie within a
certain bound and one of:
1.
Material above or below
(allows vertical stacks)
2.
Site shares face with horizontal
loc containing material and
satisfying (1)
(allows stack horiz extension)
1.
1 face of the site must
neighbour 3 horizontal locations
that each contain material
(allows gradual construction of
elevated horiz surfaces)
Queen: green, ground red, yellow: deposited
building material.
1. formation of "pillar-like" structures:
simulates reality
2. construction of walls between the pillars
Swarms for Data Mining
We have already seen how swarms of insects are able to cluster
similar objects together. Software engineers have exploited similar
ideas to cluster database records, discovering trends in, say, financial
information (which customers are likely to default on their loan), or
health data (which patients are most likely to develop heart disease).
James MacGill has developed an approach to spatial data
mining inspired by insect swarming.
Consider a map of the UK with every case of CJD marked.
Eyeballing the map will reveal the existence of clusters.
But most of these are probably just over the major UK cities,
that’s where most of the people live, after all…
We need to spot anomalous clusters – where there are more
cases than you would expect
Swarms for Data Mining
Imagine a flock of agents flying across the CJD data set…
Each agent is aware of nearby agents and of the data that
passes under it, constantly comparing the number of CJD
cases in its vicinity to the local population size.
• When it finds an anomalous “hotspot” it gets excited
• When it finds no interesting data it gets bored
• When it finds no data at all it dies
Agents that are excited slow down, agents that are bored
speed up and agents that are dead don’t move at all.
In addition, agents are attracted to nearby excited agents
and repelled by any nearby deceased agents.
How does such a system behave?
Self-Organising Data Foragers
The swarm adaptively scans the data set focusing on the interesting
parts and ignoring the boring areas.
• The swarm quickly isolates parts of the map with no cases
of CJD, littering them with dead agents which dissuade
further exploration of those areas.
• Areas where CJD cases are in line with local population
density are boring and are quickly passed over.
• Areas where the number of CJD cases in abnormally high
or low attract more and more agents of different sizes,
scanning the area at different scales and resolutions.
In comparison with exhaustively scanning the map at many
different spatial scales, the flocking approach is faster, but
perhaps slightly less reliable – MacGill suggests a hybrid
approach combing both methods.
Issues in Insect Algorithms
The development of swarm intelligence is only just beginning. Many
open questions exist…
• how can we design individual agents such that en masse,
they are able to achieve a desired swarm-level behaviour?
– should they be complex or simple?
– should they differ from one another?
– should they be reactive or non-reactive?
– should they learn? How?
– should they communicate? How?
– should they utilise stigmergy? How?
• should we worry that swarms are not predictable/reliable?
As yet we have few answers – what would a theory of
swarm intelligent systems look like?
Recap…
Last Time:
• command & control vs. selforganisation
• the sophisticated abilities of
insect colonies
• stigmergy
• algorithms inspired by insect
intelligence
–
–
–
–
Foraging
Clustering & Sorting
Building
Swarming & Flocking
This Time:
• insect-like social robots
– sorting & clustering
– cooperative transport
• Applications
– sport
– education
– entertainment
Insect-like Social Robots
So far, we have considered software algorithms inspired by the
various behaviours of social insects.
• Researchers are also implementing insect-like robot
control systems for a range of tasks. These include single
robots inspired by crickets & cockroaches, and multi-robot
systems.
• Before reviewing some of the collective behaviours that
have been achieved with multiple robot systems, we will
consider the question:
• Why take a multi-robot approach?
single complex robot working alone
versus
many simple robots working together
Multi-Robot Advantages
There are many reasons to consider building multiple simple robots
rather than a single complex solution:
• robustness – a simple machine may be less likely to fail
than a complex one
• redundancy – many robots carrying out the same task
ensures that the system can tolerate the failure of a few
• cost-effectiveness – many simple robots may be built for
the price of a single complex machine
• simplicity – we often may not know how to build a single
robot solution
• cooperation – the task may simply be impossible to carry
out by an individual
Sorting and Clustering in Reality
We have already seen the application of insect-inspired clustering
and sorting algorithms in software. Hardware implementations of
similar ideas have shown how little intelligence each agent really
needs…
At UWE Owen Holland, Chris Melhuish and colleagues, have
been developing collective robot systems for some time.
In experiments with robots the size of footballs, they have
shown that individuals with very crude sensors, no memory
or sense of spatial location, and no communication, can…
• segregate two kinds of object
• sort them into a central cluster of object A surrounded by
a ring of object B – annular sorting resembling the brood
sorting of ant species Leptothorax unifasciatus.
The Basic Sorting Task
Several mobile robots equipped with special grippers patrol an arena
littered with pucks of two kinds – red and blue.
arena
The robots can detect the gripper
pushing against anything heavier than
a puck, a gripped puck’s colour, and
whether an obstacle is directly ahead…
Three rules achieve simple clustering:
pucks
robot
with
gripper
1. if (gripper pushed & obstacle) then
turn at random
2. else if (gripper pushed) then
reverse (dropping puck) and turn at
random
3. else go forward
How Does It Work?
Notice that the robots know nothing about the location of a cluster or
even the local density of pucks.
The robots succeed at the task because…
• they pick up loose pucks without noticing
• when they have a puck, they tend to deposit it next to
another puck (pushing a puck into another triggers rule 2)
• when they don’t, they tend to only pick up pucks which
are on their own (pucks in groups trigger rule 2)
Interestingly, as in some experiments with ants, the robots
often clustered the pucks at the edge of the arena.
Through artificially varying the probability that rule 1 would
fail to fire, the researchers found that they could increase or
decrease this edge-seeking tendency.
Annular Sorting
In an attempt to speed up the clustering, the robots’ rule set was
altered slightly to treat blue and red pucks differently:
1. if (gripper pushed & obstacle) then turn at random
2. else if (gripper pushed) then
if (blue puck) then pull puck backwards endif
reverse (dropping puck) and turn at random
3. else go forward
The new robots treat the red pucks as normal, but drag blue
pucks back a small distance before letting go of them.
These robots did not cluster pucks any faster, but succeeded
in roughly arranging the blue pucks in a doughnut-shaped
ring around a cluster of red pucks – in a similar fashion to
the brood sorting Leptothorax species…
Collective Stigmergic Behaviour
These robot experiments show that what appears to be organised
collective behaviour can be achieved by very simple robots following
very simple rules:
• the robots made use of properties of their physical
environment to help them coordinate their behaviour
• they exploited the stigmergic effects of each other’s
behaviour to achieve their joint task
• the global-level effects of changes to the rules of individual
robots was very hard to predict
Through this kind of simple robot research it may be
possible to gain an understanding of…
• natural collective behaviour in insect colonies, etc.
• the nature of stigmergy itself
Natural Collaborative Transport
Social ants often transport large items to their nests in teams,
achieving feats that would be beyond a single ant.
In contrast, getting robots to work together as a team on a
joint project, as we have just seen, is a tricky task.
Bonabeau, Dorigo and Theraulaz list some of the interesting
questions that this insect behaviour suggests:
•
•
•
•
Do individuals behave differently when they are in a team?
How are enough ants recruited when help is needed?
How do ants co-operate and co-ordinate their behaviour?
How do they handle deadlocks, obstacles, conflicts, etc.?
Studying the behaviour of collective behaviour in groups of
robots is helping to answer some of these questions...
Building Robot Collaboration
In addition to helping us understand natural insect behaviour,
achieving effective robot collaborative transport techniques would
represent a significant feat of engineering.
Some engineering considerations:
• How can interference between team-mates be reduced?
• What is the optimal team size for a particular task?
• How should team-mates accommodate each other?
• How complicated must conflict resolution mechanisms be?
• How should team-mates communicate with each other?
Observation of co-operative transport in ants tends to
suggest that an efficient solution could be engineered using
a group of robots lacking explicit means of communication,
or any centralised control.
Ant-Like Robot Box Pushing
Co-operative box pushing is considered a bench-mark test for teams
of multiple robots. Many researchers have taken an approach
involving planning and explicit centralized mechanisms for conflict
resolution. In contrast, Kube and Zhang have developed an antinspired system…
The box-pushing task is as follows:
A lit box, which is too heavy for a single robot to push,
must be moved from some random location in the middle
of an arena to the arena’s edge.
The sensors on Kube & Zhang’s circular, wheeled robots can
detect i) the box, ii) other robots, and iii) nearby obstacles.
Each robot is identical and can carry out five different
behaviours: GOAL, AVOID, FOLLOW, SLOW, & FIND.
www.cs.ualberta.ca/~kube/research.html
A Subsumption Architecture
Kube & Zhang use a subsumption architecture:
obstacle?
goal?
avoid
goal
slow
robot?
follow
find
Each behaviour is triggered by a
certain stimulus and suppresses
those below it in the architecture.
The robot wanders until it
detects either another robot,
which it follows, or the goal,
which it approaches.
wheels
Encountering an obstacle at any time will trigger a simple
avoidance behaviour – turning away from the obstacle.
This controller enables robots to converge on the box. How
do they co-operate in pushing it to the wall?
Co-operative Box Pushing
The robots described so far will succeed in locating the box and
pushing it to the wall, but they are far from efficient.
Like ants, the robots suffer from periods of stagnation in
which the box makes no significant progress.
Kube and Zhang’s approach to solving this problem is
inspired by ant behaviours: realignment and repositioning.
If the box has not moved for a while a robot will realign
itself by rotating slightly.
Realignment alters the forces acting on the box while
ensuring that it is still pushed in the same general direction.
If realignment proves fruitless, a robot will reposition itself
by backing away and re-approaching the box.
This allows other robots to act on the box in its absence and
changes its influence on the box.
Box Pushers in Action…
In this movie, six
of Kube and
Zhang’s robots
attempt to push a
lit box to the side
of the arena.
As described,
periods of
stagnation cause
robots to realign,
or back off &
reposition
themselves.
Sport - Football
We have all encountered tasks that demand more sophisticated
coordination. A good example is football.
RoboCup, a football tournament for teams of robots, has
been running for a few years now and acts as an informal
way to assess the state-of-the-art in collective robotics.
- www.robocup.org
Many teams are controlled by traditional AI systems:
modelling, planning and communicating in order to coordinate players on the joint task of scoring goals.
Although no insects play anything like football, could a
swarm intelligence approach succeed at RoboCup?
This decentralised, minimally intelligent approach is clearly
lacking in sophisticated reasoning, planning, etc. But could
it make up for this through increased speed, robustness,
fault tolerance, opportunism, etc?
Education – Star Logo
http://education.mit.edu/starlogo/
The literature on research into swarm systems and collective
behaviour in general suggests that designing these systems is very
hard. They behave in counter-intuitive ways. Understanding them
requires new ideas and lateral thinking.
Are swarm systems intrinsically hard to understand, or have
we just had too little experience of building and using them?
Mitchel Resnick, in his book Turtles, Termites & Traffic Jams,
sets out to explore whether exposure to these systems can
provide young children with insights into how they behave.
His StarLogo is a programming language designed to help
users to build systems of distributed, interacting agents.
Could exposure to systems like StarLogo help to create a
new generation of thinkers who have been “liberated from
the confines of the centralised mindset”?
Example – Levels of Description
One theme running through this module is levels of description – at one
level a system may comprise many simple interacting individuals, while
at a higher level it is a single organised whole, exhibiting complex
coordinated behaviour.
The high-school students working with Resnick encountered
this idea while building a StarLogo traffic jam simulator:
While the cars were always moving forward, the jam
sometimes seemed to be moving backwards.
Some students coped well with this phenomenon – “[The
traffic jam is] like a blob, which can move either backward
or forward regardless of how the cars are moving”
Others (e.g., MIT researchers) had difficulty – “A loop [in a
StarLogo program] does the same thing every time. So the
whole thing should be repeating itself.”
Entertainment - Animation
One perhaps surprisingly successful application of swarm systems
has been in the domain of computer graphics.
Rather than animating a flock or herd or squadron or team
or crowd by hand, swarm systems are able to generate
complex, life-like sequences of behaviour automatically.
The behaviour of each member of the group is governed by
a control system which determines its movements (and
perhaps the noises or gestures that it makes), taking into
account the positions and movements of those around it.
Systems inspired by insects or simple animals, will be simple
to build and fast to simulate – perhaps fast enough to allow
the generation of animation sequences in real-time.
Behavioural Animation in
Action
Perhaps the most famous
example of this technique
is Craig Reynolds’ Boids –
simple artificial birds which
flock in a natural, life-like
manner despite being
controlled by a tiny set of
very simple rules…
http://www.red3d.com/cwr/boids/
Boids
• Basic flocking model consists of 3 simple steering
behaviours which describe how an individual boid
manoeuvres based on the positions and velocities its
nearby flockmates:
Separation: steer to avoid crowding
local flockmates
Alignment: steer towards the
average heading of local flockmates
Cohesion: steer to move toward the
average position of local flockmates