Collective Intelligence: from ants to neurons

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Collective Intelligence from ants to neurons

• Dumb parts, properly connected into a swarm,
  yield to smart results

IFAE-Thursday Meeting, 22nd February 2007
Estel Pérez
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What is this all about?

• "An individual ant is not very bright, but ants
  in a colony, operating as a collective, do
  remarkable things.
• A single neuron in the human brain can
  respond only to what the neurons connected to it
  are doing, but all of them together can be Albert
  Einstein."By Deborah M. Gordon (Stanford
  University)
• We are interested in systems
• where simple units together
• behave in complicated ways.

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Outline

• Introduction
• Complexity
• Emergence
• Examples
• Swarm Intelligence learning from Nature
• Ants
• Natural Ants How do they do it?
• Ant Colony Optimization
• Applications TSP
• Birds Fish
• Modeling Bird Flocking
• Particle Swarm Optimization
• Applications
• Conclusions

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Complexity

• Artificial Intelligence
• Neural Networks
• Chaos
• Butterfly Effect
• Attractors
• Fractals
• Self-Organization
• Non-linear systems
• Emergence
• Collective Intelligence

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Complexity

• Studies systems with many strongly-coupled
  degrees of freedom.
• Many natural, artificial and abstract objects or
  networks can be considered to be complex systems.
  
• The study of complexity is highly
  interdisciplinary.
• Examples of complex systems include ant-hills,
  human economies, climate, nervous systems, cells
  and living things, including human beings, as
  well as modern telecommunication infrastructures.

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Complexity

• All complex systems have behavioral and
  structural features in common
• Relationships are non-linear
• Relationships contain feedback loops
• Complex systems have hysteretic behavior they
  change over time, and prior states may have an
  influence on present states.
• Complex systems may be nested The components of
  a complex system may themselves be complex
  systems. (cell-organism-colony-ecosystem-Gaia)
• May produce emergent phenomena.

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Emergence

• "The whole is more than the sum of its parts
  (Aristotle)
• Definition "the arising of novel and coherent
  structures, patterns and properties during the
  process of self-organization in complex systems."
  By Goldstein
• Superficial complexity that arises from a deep
  simplicity by Murray Gell-Mann (Nobel Prize for
  the quark model)
• Bottom-up behavior Simple agents following
  simple rules generate complex structures/behaviors
  .
• Agents dont follow orders from a leader.

A termite "cathedral" mound produced by a termite
colony a classic example of emergence in nature.
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Examples

• Biology The cellular slime molds are unicellular
  organisms that usually take the form of
  individual amoebae, but under stress they
  aggregate to form a multicellular assembly.
• Model
• Slime mold gives off a substance called pheromone
  all the time.
• Local Rules
• Move in the direction that has the highest
  pheromone concentration.
• If no pheromone, move randomly.
• All the while, each slime mold cell is giving off
  a pheromone which evaporates at each time step.
• Parameters number of cells, rate of pheromone
  evaporation

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Examples

• Economics Stock market precisely regulates the
  relative prices of companies across the world,
  yet it has no leader.
• The World Wide Web is a decentralized system
  exhibiting emergent properties. The number of
  links pointing to each page follows a power law.
• Mathematics a Moebius strip has emergent
  properties it can be constructed from a set of
  two-sided, four edged, squared surfaces. Only the
  complete set of squares is one-sided and
  one-edged!
• Could Human Conscience be explained as an
  emerging behavior from the interaction of
  individual neurons ?

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Swarm Intelligence

• Based on the study of emergent collective
  intelligence of groups of simple agents

Bird Flock
Animal Herd
Ant Colony
Fish School
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Learning from Nature

• Nature has inspired researchers in many different
  ways.
• Airplanes have been designed based on the
  structures of birds' wings.
• Robots have been designed in order to imitate the
  movements of insects.
• Resistant materials have been synthesized based
  on spider webs.
• After millions of years of evolution all these
  species developed solutions for a wide range of
  problems. Some ideas can be developed by taking
  advantage of the examples that Nature offers.

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Learning from Nature

• Some social systems in Nature can present an
  intelligent collective behavior although they are
  composed by simple individuals.
• The intelligent solutions to problems naturally
  emerge from the self-organization and
  communication of these individuals.
• These systems provide important techniques that
  can be used in the development of distributed
  artificial intelligent systems.

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Swarm Intelligence

• Swarm Intelligence is an artificial intelligence
  technique based on the study of collective
  behavior in self-organized systems.
• Swarm Intelligence systems are typically made up
  of a population of simple agents interacting
  locally with one another and with their
  environment. This interaction often lead to the
  emergence of global behavior.
• The main bio-inspired algorithms that have been
  developed are
• Ant Colony Optimisation (ACO)
• Particle Swarm Optimisation (PSO)

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Natural Ants

• Individual ants are simple insects with limited
  memory and capable of performing simple actions.
• However, an ant colony expresses a complex
  collective behavior providing intelligent
  solutions to problems such as
• carrying large items
• forming bridges
• finding the shortest routes from the nest to a
  food source, prioritizing food sources based on
  their distance and ease of access.

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Natural Ants

• Moreover, in a colony each ant has its prescribed
  task, but the ants can switch tasks if the
  collective needs it.
• Outside the nest, ants can have 4 different
  tasks
• Foraging searching for and retrieving food
• Patrolling looking for food supply
• Midden work Sorting the colony refuse pile
• Nest maintenance work construction and clearing
  of chambers
• An ants decision whether to perform a task
  depends on
• The Phisical State of the environment
• If part of the nest is damaged, more ants do nest
  maintenance work to repair it
• Social Interactions with other ants

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How Do Social Insects AchieveSelf-organization?

• Communication is necessary
• Two types of communication
• Direct antennation, trophallaxis (food or liquid
  exchange), mandibular contact, visual contact,
  chemical contact, etc.
• Indirect two individuals interact indirectly
  when one of them modifies the environment and the
  other responds to the new environment at a later
  time. This is called stigmergy.

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Natural ants How do they do it?

• How do they know which task to perform?
• When ants meet, they touch with their antennae,
  that are organs of chemical perception.
• An ant can perceive the colony-specific odor that
  all nest mates share.
• In addition to this odor, ants have an odor
  specific to their task, because of the
  temperature and humidity conditions in which it
  works.
• So that an ant can evaluate its rate of encounter
  with ants of a certain task.
• The pattern of interaction each ant experiences
  influences the probability it will perform a task.

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Natural ants How do they do it?

• How can they manage to find the shortest path?
• "The best possible way for ants to find anything
  is to have an ant everywhere all the time,
  because if it doesn't happen close to an ant, 
  they are not going to know about it. Of course
  there are not enough ants in the colony, so the
  ants have to move around in a pattern that allows
  them to cover space efficiently"

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Natural ants How do they do it?

• They establish indirect communication system
  based on the deposition of pheromone over the
  path they follow.
• An isolated ant moves at random, but when it
  finds a pheromone trail, there is a high
  probability that this ant will decide to follow
  the trail.
• An ant foraging for food deposits pheromone over
  its route. When it finds a food source, it
  returns to the nest reinforcing its trail.
• So, other ants have greater probability to start
  following this trail and thereby laying more
  pheromone on it.
• This process works as a positive feedback loop
  system because the higher the intensity of the
  pheromone over a trail, the higher the
  probability of an ant start traveling through it.

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Natural ants How do they do it?

• Since the route B is shorter, the ants on this
  path will complete the travel more times and
  thereby lay more pheromone over it.

• The pheromone concentration on trail B will
  increase at a higher rate than on A, and soon the
  ants on route A will choose to follow route B
• Since most ants will no longer travel on route A,
  and since the pheromone is volatile, trail A will
  start evaporating
• Only the shortest route will remain!

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Natural ants Experiments
(1)

• (1) Ants finished all using the same path (each
  one of the 2 paths, 50 of times)
• (2) Ants use the short path
• (3) Ants get to find the shortest path

(2)
(3)
(1)
(2)
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Modeling Ants Colony

• It is known that the ability of ants in finding
  the shortest route between the nest and a food
  source can be used to solve graph problems.
• Environment
• Actions that an agent performs
• In a city, it chooses a route based on the
  intensity of the pheromone over the available
  paths
• When it finds the food source, it starts the
  return travel on its own pheromone trail
• All actions require only local information and
  short memory

Graph Natural Ants Model
Individual Ants Agents
Nodes Places where ants can stop Cities
Edges Routes
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Ant Colony Optimization

• Optimization Technique Proposed by Marco Dorigo
  in the early 90
• Each artificial ant is a probabilistic mechanism
  that constructs a solution to the problem, using
• Artificial pheromone deposition
• Heuristic information pheromone trails, already
  visited cities memory
• Differences between real
• and artificial ants
• Artificial ants live in a
• discrete world
• The pheromone is updated
• only after a solution has
• been constructed.
• Additional mechanisms

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Ant Colony Optimization

• ConstructAntSolutions
• The exact rules for the probabilistic choice of
  solution components vary across different ACO
  variants.
• UpdatePheromones
• It is used to increase the pheromone values
  associated with good or promising solutions, and
  decrease those that are associated with bad ones.
• Decreasing all the pheromone values through
  pheromone evaporation -gt allows forgetting-gt
  favors exploration of new areas
• Increasing the pheromone levels associated with a
  chosen set of good solutions -gt makes the
  algorithm converge to a solution
• Supd is the set of solutions that are used for
  the update
• ? (0 1 is a parameter called evaporation rate
• F is a function commonly called the fitness
  function.

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Ant Colony Optimization

• Different ACO algorithms differ in the way they
  update the pheromone.
• AS-update Supd Siter (the set of solutions
  that were constructed in the current iteration)
  -gt Like in Nature
• IB-update Supd Sib arg max F(s)
  (iteration-best solution the best solution in
  the current iteration)
• introduces a much stronger bias towards the good
  solutions -gt increases speed
• Increases the probability of premature
  convergence
• BS-update Supd Sbs (best-so-far solution the
  best solution since the first algorithm
  iteration)
• Introduces an even stronger bias
• In practice, ACO algorithms that use variations
  of the IB-update or the BS-update rules and that
  additionally include mechanisms to avoid
  premature convergence, achieve better results
  than those that use the AS-update rule.

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Applications

• The ACO can be used to solve graph problems such
  as the Traveling Salesman Problem (TSP).
• Of High computational complexity
• For which the exact algorithms are inefficient
• For which we dont need the best solution but a
  good one.

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Traveling Salesman Problem

• Given a number of cities and the costs of
  traveling from any city to any other city, what
  is the cheapest round-trip route that visits each
  city exactly once and then returns to the
  starting city?
• Trying all possible solutions means n!
  permutations.
• Using the techniques of dynamic programming, it
  can be solved in time O(n22n)
• The problem is of considerable
• practical importance. Example
• printed circuit manufacturing
• scheduling of a route of the drill
• machine to drill holes in a PCB.

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TSP solved using ACO I shoud have an applet,
but
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Modeling bird flocking

• The synchrony of flocking behavior is thought to
  be a function of birds efforts to maintain an
  optimal distance between themselves and their
  neighbors.
• Individual members can profit from the
  discoveries and previous experience of other
  members during the search for food. This
  advantage can become decisive , overweighting the
  disadvantages of competition for food
• Birds and fish adjust their physical movement to
  avoid predators, seek for food and mates.
• Humans tend to adjust our beliefs and attitudes
  to conform with those of our social peers. Humans
  change in abstract multidimensional space,
  collision-free.

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Modeling bird flocking

• Definitions
• Flock is a group of objects that exhibit the
  general class of polarized (aligned),
  non-colliding, aggregate motion.
• Boid is a simulated bird-like object, i.e., it
  exhibits this type of behavior. It can be a fish,
  bee, dinosaur, etc.
• Rules for flocking
• Cohesion Each boid fly towards the centroid of
  its local flock mates (that is, boid in its local
  neighborhood)
• Separation Each boid keep a certain distance
  away from local flock mates to avoid collisions
• Alignment Each boid align its velocity vector
  and keep velocity magnitude similar with that of
  the local flock
• Note There might be many other rules for making
  the flock more realistic.

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Particle Swarm Optimisation

• Proposed by Eberhart and Kennedy in the middle
  90
• Global Optimization Algorithm dealing with
  problems in which a best solution can be
  represented as a point or surface in an
  n-dimensional space.
• Inspired in the social behavior of bird flocks
  and fish schools
• The main application is on Numeric Optimization
• Advantages
• It requires only primitive mathematical operators
• Is computationally inexpensive, in terms of both
• Memory requirements
• Speed
• The large number of members that make up the
  particle swarm make the technique impressively
  resistant to the problem of local minima.

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Particle Swarm Optimisation

• Imagine a birds flock in an area where there is
  a single food source.
• A bird dont know where the food is, but it knows
  its distance to the food.
• The best strategy is to follow the bird that is
  closer to the food.
• Particles save and communicate the best solution
  they have found.

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Particle Swarm Optimisation

• It considers a particle swarm (or cloud) that
  moves over the solution space, and particles are
  evaluated according to some fitness criterion.
  The movement of each particle depends on
• Its best position since the algorithm started
  (pBest)
• The best position of the particles around it
  (lBest) or of the whole group (gBest)
• On each iteration, the particle changes its
  velocity towards pBest and lBest/gBest.
• So the swarm explores the solution space looking
  for promising zones.

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Particle Swarm Optimisation

• The pseudo code of the procedure is as
  followsFor each particle     Initialize
  particleENDDo
•     For each particle         Calculate fitness
  value        If the fitness value is better than
  the best fitness value (pBest) in
  history            set current value as the new
  pBest    End    Choose the particle with the
  best fitness value of all the particles as the
  gBest
•     For each particle         Calculate
  particle velocity according equation (a)       
  Update particle position according equation
  (b)    End
• While maximum iterations or minimum error
  criteria is not attained

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Particle Swarm Optimisation

• Combination of gBest and the pBest need a
  compromise
• lBest can be
• Social the particles around are always the same,
  no matter where they are in space
• Geographical the particles around are those
  whose distance is the shortest
• Global PSO vs. Local PSO the global version
  converges quickly to a solution but it gets more
  easily stuck in local minima.

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Swarm TechnologyApplications

• Swarm technology is particularly attractive
  because it is
• cheap
• robust
• Simple
• Some examples of applications
• Controlling unmanned vehicles
• Possibility of using it to control nanobots
  within the body to kill cancer tumors
• Disney's The Lion King was the first movie to
  make use of swarm technology (the stampede of the
  wildebeests scene). The Lord of the rings used it
  too during the battle scenes.
• Grid Data Replication

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Conclusions

• We can learn from nature and take advantage of
  the problems that she has already solved.
• Many simple individuals interacting with each
  other can make a global behavior emerge.
• Techniques based on natural collective behavior
  (Swarm Intelligence) are interesting as they are
  cheap, robust, and simple.
• They have lots of different applications.
• Swarm intelligence is an active field in
  Artificial Intelligence, many studies are going
  on.

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Thats all!

• Thank you for your attention