Bioinspired Computing Lecture 3

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Bioinspired ComputingLecture 3

• Collective Behaviour and
• Swarm Intelligence

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Overview

• This Time
• command control vs. self-organisation
• 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

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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

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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

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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  out of date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, self-organising systems such as
amazon.coms often enjoy advantages over their
command and control cousins
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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?

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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.
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E.g.
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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 todays 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.

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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

As ants forage they deposit a trail of slowly
evaporating pheromone.
Those that reach the food first return 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.

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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.

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.
B
A
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.
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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.

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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
  ants local environment
• items are more likely to be picked up when they
  differ from those around them, and
• 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.

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Ants for Catering

• Imagine youre want to seat many guests. Its
  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.
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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.
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Ants for Architecture

• How can insects in a colony coordinate their
  behaviour in order to build highly complex
  architectures? Ants and termites dont appear to
  have blueprints in their heads they seem to
  follow simple rules in an almost random manner.

If the blueprint isnt 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
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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.
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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.
BT 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
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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,
thats where most of the people live, after
all We need to spot anomalous clusters where
there are more cases than you would expect
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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.
Agents that are excited slow down, agents that
are bored speed up and agents that are dead dont
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?
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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.
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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?