Research Trends in Artificial Intelligence

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Research Trends in Artificial Intelligence

• Muhammad Younas 05020110
• Hassan Javid 05020304
• Danish Shah 06020225

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Maze Solving RobotRobots Tasks

• The robot is a simple machine. It can move north,
  south, east or west (NSE or W).
• It has no sensory input and does not have
  conditional moves its behavior is determined by
  a list of which moves it is to take while in the
  maze.
• The maze has one entrance (where the robot
  starts) and one goal (the robot's goal).
• The closer the robot gets to the goal in the
  fewest number of moves, the better its score will
  be.

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Maze Solving RobotRobots Tasks

• The robot will simply be represented as a list of
  moves. e.g (n e e s s) etc. Maze Size can be
  variable. Such as a 44 maze can look like this.
  E is the entrance, and R is where the robot
  current position after executing the moves given
  above. Since the robot cannot see anything, it
  merely moves until it reaches the goal. How close
  it gets to the goal determines its score. If it
  reaches the goal, it gets a high score and extra
  pointes are awarded when it reaches the goal
  with the fewer steps.

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Maze Solving RobotDefinitions

• Genome The list of a population member's genes.
  Genes are 1s and 0s. For example(1 0 0 1 1 0 1
  1)
• Phenome
• Phenome is an expression of an individual's
  genome in the "world." In this case, the phenome
  is the form of what's being evolved, or a
  description of the behavior of the individual,
  and it comes directly from the genome. The
  robot's genome would be a list of its moves,
  decoded from the genome.
• If every 2 bits in the genome encoded for one
  move (00 n, 01 s, 10 e, 11 w), then the
  above genome's phenome would be
• GENOME (1 0 0 1 1 0 1 1)
• PHENOME(e s e w )

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Maze Solving RobotDefinitions

• FitnessFitness tells how good a genome is.
  Genomes are sometimes scored directly, but often
  they are converted into a phenome and the phenome
  is given a score. For example in GENOME (1 0 0
  1 1 0 1 1) that is PHENOME( e s e
  w ) if the robot's moves of e, s, e, w lead it
  closer to the exit, it would have a higher score
  than one that leads it to some dark corner of the
  maze far away from the goal.
• Individual Genetic Algorithms will operate on
  individuals. An individual will be a list of the
  fitness, genome and corresponding phenome. For
  example
• (4 (1 0 0 1 1 0 1 1) (e s e w))
• Population is group of individuals. The GA
  operates on these, producing their phenomes from
  genomes, evaluating each one's fitness, then
  selecting mates and producing new individuals for
  the next generation. This will be a list of
  individuals.

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Maze Solving Robot The Genetic Algorithm

• The Genetic Algorithm will begin with an initial
  population with randomly generated genomes.
• It generates phenomes for each genome, then
  evaluates each individual's fitness based on its
  phenome.
• During each run, we will apply the genetic
  operations and delete/create new individuals.

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Maze Solving Robot The Genetic Algorithm

• Some of the routines that we have outlined and
  implemented are so far are.
• MutateTakes a genome, returns the same genome
  with some of the bits flipped.
• Cross Over Takes 2 genomes, performs one-point
  crossover on them to produce two new genomes.
• Mate
• Takes 2 individuals and performs crossover on
  their genomes to get 2 new genomes. It then
  mutates the new genomes. Finally, it makes 2 new
  individuals with dummy values for fitness and
  phenome.

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Maze Solving Robot The Genetic Algorithm

• Selecting an IndividualTakes a population of
  individuals. Chooses a single individual randomly
  and returns that individual. This random choice
  is based on the fitness value of a genome in the
  population
• Random Population
• Takes a population size and number of genes in
  each genome and generates a population of
  individuals with random genomes
• Random Genome
• This function takes a genome-length and returns
  a random genome of that length.

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Maze Solving Robot The Genetic Algorithm

• We are almost done with the research for our
  project and we have outlines almost complete
  pseudo code.
• We have started working on implementation and
  have implemented some of the routines (functions)
  that were listed before.
• Hopefully by next presentation we will come up
  with more routines implemented.

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References

• Embodied Intelligence at www.ai.mit.edu.
• Genetic Algorithms at genetic algorithm_at_Everything
  2.com
• Maze Solving Algorithms at www.aboutAI.net.
• Artificial Intelligence at www.indiana.edu
• John Knight page at www.doe.carleton.ca.
• Tony Pipes page at www.ias.uwe.ac.uk.