Page 64 - DCAP506_ARTIFICIAL_INTELLIGENCE

Artificial Intelligence

Notes
Example: Take the example of planning for an office worker. Let we have a different
table of three rules:
1. If in out current state we are starving, and in our objective state we are not starving, then
either the “visit hotel” or “visit Canteen “ operator is suggested.

2. If our current state we do not have money, and if in your goal state we have money, then
the “Visit our bank” operator or the “Visit secretary” operator is suggested.
3. If our current state we do not identify where something is needed in our target state we do
recognize, then either the “visit office enquiry”, “visit secretary” or “visit co worker “
operator is suggested.

Self Assessment

Fill in the blanks:
14. The ................................. process centers about locating the difference among current state
and goal state.

15. Means- ends analysis is functional for many ................................. activities.
4.7 Summary

 Heuristic techniques are known as weak methods, as they are susceptible to combinatorial
explosion.

 The generate and Test algorithm is a depth first search practice since complete possible
solutions are produced before test.

 In hill climbing technique, a feedback is utilized here to decide on the course of movement
in the search space. In the depth-first search, the test function will just accept or reject a
solution.

 Best First Search is a amalgamation of depth first and breadth first searches.
 Greedy best-first Search (GBFS) develop node closest to the goal; choose path with lowest
h(n) and evaluate nodes by using heuristic function – that is, f(n) = h(n).

 Many troubles in AI can be regarded as problems of constraint satisfaction, where the goal
state pleases a specified set of constraint.
 The means-ends analysis process centers about locating the difference among current state
and goal state.
 Means-ends analysis is functional for many human planning activities.

4.8 Keywords

Best First Search: Best First Search is a amalgamation of depth first and breadth first searches.

Constraint Satisfaction: Many troubles in AI can be regarded as problems of constraint
satisfaction, where the goal state pleases a specified set of constraint.
Generate and Test Algorithm: It is a depth first search practice since complete possible solutions
are produced before test.

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