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Unit 4: Heuristic Search Techniques

E = 4 Notes
D = 1
R = 6
S = 3
C1 = 0
C2 = 0
C3 = 0
C4 = 0

C4(0) C3 C2(0) C1(0)
C9 R(6) O(2) S(3) S(3)
+ R6 O(2) A(5) D(1) S(3)
D(1) A(5) N(8) G(7) E(4) R(6)

Self Assessment

Fill in the blanks:
12. Many troubles in AI can be regarded as problems of .................................... where the goal
state pleases a specified set of constraint.
13. Constraint satisfaction problems can be solved by means of any of the ....................................
approaches.

4.6 Means-ends Analysis

Many of the search strategies either reason forward of backward however, frequently a mixture
o the two directions is suitable. Such assorted strategy would make it probable to solve the
major parts of problem first and solve the lesser problems occur when combining them together.
Such a technique is known as “Means-ends Analysis”.
The problem space of means-ends analysis has an early state and one or more objective state, a
set of operate with a set of preconditions their application and difference functions that calculates
the difference among two state a(i) and s(j).

Did u know? The means-ends analysis process centers about locating the difference among
current state and goal state.
A problem is solved by means of means-ends analysis by:
1. Calculating the current state s1 to a target state s2 and computing their difference D12.

2. Satisfy the preconditions for some suggested operator op is selected, then to reduce the
difference D12.
3. The operator OP is applied if probable. If not the present state is solved a goal is formed
and means- ends analysis is applied recursively to decrease the sub goal.
4. If the sub goal is solved state is reinstated and work resumed on the original problem.
(The first AI program to use means-ends analysis was the GPS General problem solver)
Means-ends analysis is functional for many human planning activities.

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