Chapter 3 Heuristic Search Techniques (continue)

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Chapter 3Heuristic Search Techniques (continue)

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Constraint Satisfaction

• limit on time, cost, materials
• reduce search space
• process
• step 1. constraint are discover and propagate as
  far as possible
• if no solution found ? search again
• step 2. Guess about something is made to set a
  new constraint and propagate with the new
  constraint
• EX N E 1 ..................(1)
• N 3 ..................(2)
• E 2 ..................(3)

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Constraint Satisfaction

• To terminate the current path
• 1. found contradiction
• If we found contradiction ? then we know that
  there is no solution
• 2. propagation run out of stream or no further
  changes that can be made on the basis of current
  basis.
• backtracking will be use to find a new path
  when found contradiction in the current path or
  no further change in the current path..
• more powerful rules ? less guessing needed

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Constraint Satisfaction

• constraint
• 1) simple list of possible value for a single
  object ? dynamic value
• ? describe explicitly in each problem state
• 2) complex describe relationship between or
  among objects
• ? dynamic value or
• ? static value ex. waltz problem
  
• 
  p.374
• DDB dependencydirected backtracking p.211

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Constraint Satisfaction Algorithm
p. 90
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Cryptarithmetic Problem
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Means-Ends Analysis

• Allow mixed strategy forward chaining and
  backward chaining
• Operator subgoal/ priority level for each subgoal
• recursive process
• GPS general problem solver Newell and Simon
  1963
• not suitable for very large problem
• more example of clock world is in chapter 13, p.
  332

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Means-Ends Analysis

• Rules consist of figure 3.15
• Left side PRECONDITION
• Right side describe problem state that will be
  changed by the application
  of the rule.

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Means-Ends Analysis

• PROBLEM TO SOLVE ROBOT
• Moving a desk with two things on it from one
  room to another.......
• they may be more than one operator to do the job
  for reducing the difference
• carry small object
• push large object
• change size of object operator saw_apart ?

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Means-Ends Analysis
The End

• Difference Table figure 3.16

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Means-Ends Analysis
chair
C to E ?
chair

• C to E WALK PICKUP CARRY

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Means-Ends Analysis
p. 97
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Summary
The End

• The difference among the proposed algorithms
• How, at each state of the search process, a state
  is selected for expansion.
• How operator to be apply to that nose is
  selected.
• Whether an optimal solution can be guaranteed.
• Whether a given state may end up being considered
  more than once.
• How many search descriptions must be maintained
  throughout the search process.
• Under what circumstances should a particular
  search path be abandoned.

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END Chapter 3
The End
The road to success is always
under construction
Jim Miller