Means-Ends Analysis

• Allows both backward and forward searching.
• This means we could solve major parts of a problem first and then return to smaller problems when assembling the final solution.
• GPS was the first AI program to exploit means-ends analysis.
• STRIPS (A robot Planner) is an advanced problem solver that incorporates means-ends analysis and other techniques.

Very loosely the means-ends analysis algorithm is:

1. Until the goal is reached or no more procedures are available:
   • Describe the current state, the goal state and the differences between the two.
   • Use the difference the describe a procedure that will hopefully get nearer to goal.
   • Use the procedure and update current state.
2. If goal is reached then success otherwise fail.

Constraint Satisfaction

• The general problem is to find a solution that satisfies a set of constraints.
• heuristics used not to estimate the distance to the goal but to decide what node to expand nest.
• Examples of this technique are design problem, labelling graphs, robot path planning and cryptarithmetic puzzles (see last year).

Algorithm:

1. Propagate available constraints:
   • Open all objects that must be assigned values in a complete solution.
   • Repeat until inconsistency or all objects assigned valid valid values:
     • select an object and strengthen as much as possible the set of constraints that apply to object.
     • If set of constraints different from previous set then open all objects that share any of these constraints.
     • remove selected object.
2. If union of constraints discovered above defines a solution return solution.
3. If union of constraints discovered above defines a contradiction return failure
4. Make a guess in order to proceed. Repeat until a solution is found or all possible solutions exhausted:

1. select an object with a no assigned value and try to strengthen its constraints.
2. recursively invoke constraint satisfaction with the current set of constraints plus the selected strengthening constraint.