Robotics Versus Artificial Intelligence. Search

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Robotics VersusArtificial Intelligence. Search
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Search

• All AI is search
• Game theory
• Problem spaces
• Every problem is a feature space of all possible
  (successful or unsuccessful) solutions.
• The trick is to find an efficient search strategy
  in this space.

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Example of an Intelligent Action

• Getting ready to come to class
• Describe so a machine could do it
• search among alternatives (car or bus)
• represent the knowledge

This requires a lot of knowledge!
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Search

• Example Four three-letter crossword puzzle
• Search problem is find correct puzzle
• Approaches
• word fill
• take word,
• put it to space,
• if contradiction, backtrack
• space fill
• take vertical or horizontal space
• select a word with this length
• put it into space,
• if contradiction, backtrack
• Many other strategies homework, find space and
  operators in it, discuss backtracking strategy

Cat dog cam may mom sit mit
C A M
A O
T O M
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Blind Search

• Search depends only on nodes position in the
  search tree
• Two basic blind searches
• depth-first
• breadth-first

• Problem define depth-first search for the above
  problem
• Problem define breadth-first algorithm for the
  above problem.
• Repeat both for each of the approaches outlined
  above.

Called also search strategies
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Depth-First Search Pseudo-Code

• 1. Set L to list of initial nodes
• 2. n head(L), Empty(L) gt fail
• 3. If ngoal, stop, return it and return the path
  leading to it
• 4. pop(L), push(L) all ns children,
• 5. go to step 2

Depth First is based on a stack, L
Head car in LISP
empty null in LISP
push(L)
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Breadth-First Search

• 1. Set L to list of initial nodes
• 2. nhead(L), Empty(L)gt fail
• 3. If ngoal then stop and return it and path
• 4. Dequeue(L), Enqueue(L) all ns children
• 5. Go to step 2

Depth First is based on a queue, L
Means, remove from queue
Means, add to queue
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Heuristic Search

• Meta-level reasoning
• heuristic function aids in selecting which part
  of search tree to expand
• trade-off between time to compute heuristic
  function and to expand the tree

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Other Issues

• Backtracking
• chronological backtracking as in Prolog
• dependency-directed backtracking
• Search direction
• forward (toward goal)
• backward (from goal)
• and math proving problems
• Bi-directional
• and building tunnel story

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Search Examples I

• Game playing
• chess
• backgammon
• Finding path to goal
• Missionaries and cannibals
• Towers of Hanoi
• Sliding Tile games (15, 8) , puzzles

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Search Examples II

• finding a goal
• cryptoarithmetic
• n-queens
• mutilated checkboard or tough nut of McCarthy
  problem.

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Example Applications of search

• Expert Systems
• Natural Language Processing
• Vision
• Robotics

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Search Game Theory
tic-tac-toe,
9!1 362,880
Robot interaction with humans, other robots and
environment can be described in terms of a game
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Game playing

• Programs that
• take advantage of the computer's ability to
  examine a large number of possible moves in a
  short period of time and
• logically assess their probable success or
  failure
• have been already developed
• They were used for game playing in
• tic-tac-toe,
• checkers,
• chess,
• and the Oriental game called Go.

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Recent Trends in Artificial Intelligence

• Intelligent agents
• Experimental AI software designed to sift through
  masses of information made available on the
  evolving Information Superhighway of cyberspace
  to suggest topics of interest or importance for
  an individual.
• Artificial life
• A field of AI research that studies the adaptive
  control systems of insects and other ecological
  systems and reproduces them in robotic
  insectoids.
• More recently researchers have been working on
  robots with the intelligence of a two-year-old
  child.
• Intelligent Agents and Artificial Life are now
  part of robotics

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Your tasks

• Search give an example of search problem that
  can be solved using Lisp and that has not been
  presented so far in the class
• Blind and informed search for your problem, give
  an example of blind search and informed search.
  Create a powerful heuristic to solve it.
• Games examples of games include tic-tac-toe,
  checkers, chess, go, othello, etc. What kind of
  game can be a good choice for our robot-guard in
  the FAB building environment?
• Expert system what kind of expert knowledge is
  neede for our walking guard, in addition to
  knowledge about the FAB building geometry,
  distances, what is in which office, structure of
  ECE department and its people, etc.?
• How is this knowledge stored and accessed?

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Possible Lisp Problems for the exam

• 1. Mouse in Labyrinth design a Lisp algorithm
  that will be able to go out of every labyrinth.
  Discuss the program strategy versus some
  knowledge of the labyrinth geometry. What kind of
  concepts introduced so far in the class may be
  useful? Discuss using depth-first, breadth-first
  search and other search strategies.
• 2. Obstacle Avoiding robot design a Lisp program
  that will simulate a turtle avoiding obstacles.
  In contrast to your homework 2, however, the
  turtle can recognize type of the obstacle and
  select the best strategy. The best strategy is
  not necessarily to follow the outline of the
  obstacle, but to go straigth between two
  obstacles, sometimes closer to one of them. This
  is a useful subproblem of avoiding stationary
  obstacles in the corridor or room for our robot.
• A program like this was written by Mike Burns.
  Read about Voronoir diagrams and their uses in
  robotics.
• Think how these diagrams can be improved if you
  have more knowledge of obstacles.