CI Technologies

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CI Technologies
CITS7212 Computational Intelligence
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CI technologies

• CITS7212 covers eight important CI technologies
• Evolutionary algorithms
• Neural networks
• Particle swarm optimisation
• Ant colony optimisation
• The first four technologies will be used for the
  project

• Learning classifier systems
• Artificial immune systems
• Bayesian reasoning
• Fuzzy logic

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Evolutionary algorithms

• Luigi

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Neural networks

• Luigi

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Particle swarm optimisation

• A population-based stochastic optimisation
  technique
• Eberhart and Kennedy, 1995
• Inspired by bird-flocking
• Imagine a flock of birds searching a landscape
  for food
• Each bird is currently at some point in the
  landscape
• Each bird flies continually over the landscape
• Each bird remembers where it has been and how
  much food was there
• Each bird is influenced by the findings of the
  other birds
• Collectively the birds explore the landscape and
  share the resulting food

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PSO

• For our purposes
• The landscape represents the possible solutions
  to a problem (i.e. the search space)
• Time moves in discrete steps called generations
• At a given generation, each bird has a position
  in the landscape and a velocity
• Each bird knows
• Which point it has visited that scored the best
  (its personal best pbest)
• Which point visited by any bird scored the best
  (the global best gbest)
• At each generation, for each bird
• Update (stochastically) its velocity v, favouring
  pbest and gbest
• Use v to update its position
• Update pbest and gbest as appropriate

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PSO

• Initialisation can be by many means, but often is
  just done randomly
• Termination criteria also vary, but often
  termination is either
• After a fixed number of generations, or
• After convergence is achieved, e.g. if gbest
  doesnt improve for a while
• After a solution is discovered that is better
  than a given standard
• Performance-wise
• A large population usually gives better results
• A large number of generations gives better
  results
• But both obviously have computational costs
• Clearly an evolutionary searching algorithm, but
  co-operation is via gbest, rather than via
  crossover and survival as in EAs

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Ant colony optimisation

• Another population-based stochastic optimisation
  technique
• Dorigo et al., 1996
• Inspired by colonies of ants communicating via
  pheromones
• Imagine a colony of ants with a choice of two
  paths around an obstacle
• A shorter path ABXCD vs. a longer path ABYCD
• Each ant chooses a path probabilistically wrt the
  amount of pheromone on each
• Each ant lays pheromone as it moves along its
  chosen path
• Initially 50 of ants go each way, but the ants
  going via X take a shorter time, therefore more
  pheromone is laid on that path
• Later ants are biased towards ABXCD by this
  pheromone, which reinforces the process
• Eventually almost all ants will choose ABXCD
• Pheromone evaporates over time to allow
  adaptation to changing situations

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ACO

• The key points are that
• Paths with more pheromone are more likely to be
  chosen by later ants
• Shorter/better paths are likely to have more
  pheromone
• Therefore shorter/better paths are likely to be
  favoured over time
• But the stochastic routing and the evaporation
  means that new paths can be explored

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ACO

• Consider the application of ACO to the Traveling
  Salesman Problem
• Given n cities, find the shortest tour that
  visits each city exactly once
• Given m ants, each starting from a random city
• In each iteration, each ant chooses a city it
  hasnt visited yet
• Ants choose cities probabilistically, favouring
  links with more pheromone
• After n iterations (i.e. one cycle), all ants
  have done a complete tour, and they all lay
  pheromone on each link they used
• The shorter an ants tour, the more pheromone it
  lays on each link
• In subsequent cycles, ants tend to favour links
  that contributed to short tours in earlier
  cycles
• The shortest tour found so far is recorded and
  updated appropriately
• Initialisation and termination are performed
  similarly to PSO

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Learning classifier systems

• Luigi

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Artificial immune systems

• Luigi

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Bayesian reasoning

• A way of updating probabilistic hypotheses in the
  light of experience, and making decisions from
  those hypotheses
• Bayes, 1763
• Given a prior probability of a hypothesis h being
  correct, and the result of an experiment on h,
  Bayes Theorem tells us how the probability of h
  being correct should be updated
• The theorem relates
• The probability that h is correct given that an
  event e happens (P(he)), with
• The probability that e will happen if h is
  correct (P(eh))
• Dont confuse these! Contrast the probability
  that a random Australian speaks English with the
  probability that a random English-speaker is
  Australian

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BR

• Imagine a disease which affects 1/1,000 people
• A test for this disease is 99 accurate
• i.e. 99 of sufferers test positive and 99 of
  healthy people test negative
• If a person with no other symptoms tests
  positive, what is the probability that they have
  the disease?
• The prior probability P(ill) 0.001
• P(positive ill) P(negative healthy) 0.99
• P(positive) (0.999 0.01) (0.001 0.99)
  0.011
• By Bayes Theorem, P(ill positive) P(positive
  ill) P(ill) / P(positive) 0.99 0.001
  / 0.011 0.09 9
• P(ill two positives) 0.99 0.09 / 0.91
  0.01 0.09 0.99 91

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BR

• A Bayesian network is a directed acyclic graph
  that represents a set of random variables and
  their conditional interdependencies
• The structure of the network can either be
  specified by an expert, or inferred by a machine
  learning algorithm from data
• The probability associated with each node is
  updated using Bayes Theorem or other probability
  calculations
• This means that the probabilities in the network
  can be updated in the light of new knowledge or
  changed circumstances

Burglary
Alarm
Neighbour call
Radio ann.
Earthquake
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Fuzzy systems

• Fuzzy logic facilitates the definition of control
  systems that can make good decisions from noisy,
  imprecise, or partial information
• Zadeh, 1973
• Two key concepts
• Graduation everything is a matter of degree,
  e.g. it can be not cold, or a bit cold, or
  a lot cold, or
• Granulation everything is clumped, e.g. age is
  young, middle-aged, or old

young
1
old
middle-aged
0
age
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FS

• A fuzzy control system is a collection of rules
• IF X AND Y THEN Z
• e.g. IF cold AND warming-up THEN open heating
  valve slightly
• Such rules are usually derived empirically from
  experience, rather than from the system itself
• Attempt to mimic human-style logic
• Granulation means that the exact values of any
  constants (e.g. where does cold start/end?) are
  less important

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FS

• e.g. a fuzzy climate-control system

temperature
Cold
Right
Hot
no change
heat
heat
-ve
no change
heat
cool
d(temperature) / dt
zero
no change
cool
cool
ve