Constraint Satisfaction Not all problems are solved by a sequential series of steps How do we solve other types of problems Problem structure • Some problems are by ert554898


									Constraint Satisfaction

Not all problems are solved by a
   sequential series of steps.
How do we solve other types of
            Problem structure

• Some problems are structured such that you
  need to make a proper sequence of moves to
  solve the problem
     • Driving a car
     • Missionaries and cannibals
• Not all are, though. For some, what matters
  is the end goal, not how you get there
     • 8 queens
     • Choosing a college
 Constraint satisfaction problems
• In CSPs, we are simply looking for a
  solution to the problem where a set of
  variables all have values that are valid
  within the set of constraints that defines the
  goal state.
• Examples:
     • Map coloring
     • Cryptarithmetic
           Searching in CSPs
• Because the order of moves doesn’t matter,
  “searching” in a CSP is very different.

• Instead of searching a tree that is essentially
  pre-defined, with each level representing a
  move, now we search a tree we generate,
  where each level contains the possible
  values for the variables.
           How best to search?
• One way of searching a CSP is to use our previous
  search algorithms.
• The first step is to pick a value for one of the
• The next step picks a value for another variable,
• Problem: For the first level, the number of child
  nodes is the number of variables  the average
  number of values per variable.
      • This creates a tree with a huge number of nodes! Way more
        nodes than all the possible combinations.
     Efficient searching in CSPs
• Clearly, we only gain a benefit over testing all
  possible variable-value combinations if we check
  fewer nodes than that.
• The easiest way is to have each level in the search
  tree correspond to assigning a value to only one
• But how do we decide which variable to use first?
   – This is an important question because if we choose
     poorly, we may over constrain ourselves
   – The goal should be to find a choice that both minimizes
     branchiness, while leaving us with the most options
    Minimum Remaining Values
• Choose the remaining variable with the least
  number of possible values.
  – Minimizes branchiness because this variable will
    have the fewest branches at a given depth of the
  – Unclear how it affects depth since it doesn’t take
    into account how this choice affects other
            Degree heuristic
• Choose the one that is involved in the
  largest number of other constraints.
  – Reduces branchiness by pruning the number of
    possibilities for other variables
  – Can over-prune, so works best when used
    primarily to break ties in the MRV heuristic
      Least-constraining value
• Once we’ve chosen a variable, how do we
  choose a value?

• Choose the value that rules out the fewest
  choices (prunes the fewest branches from
  the tree).
            Forward checking
• So far, we’ve only looked at constraints at the time
  we chose a particular variable-value combination.
• Wouldn’t it be easier to look ahead and see the
  results of given choices on the constraints?
• Forward checking works by, every time we
  choose a value for a variable, it eliminates the
  values for other variables that conflict with that
• In combination with MRV, yields the most
  efficient constraint satisfaction system.

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