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Heuristic Search Strategies:
Generally speaking that, a heuristic search is one which have uses a rule of thumb to improve an agent's performance in solving the problems via search. A heuristic search is not to be confused with a heuristic measure. If you can justify a heuristic measure, then it can opens up a range of generic heuristic searches that you can try to improve your agent's performance, as discussed below. It is worth recalling, in fact, that any rule of thumb, for instance, choosing the instruction of operators whenever applied in a simple breadth first search, it is a heuristic.
In terms of our agenda analogy we can say that, a heuristic search chooses where to put a (state, operator) pair on the agenda that it is proposed as a move in the state space. This choice can be fairly complicated and based on many factors. In terms of the graph analogy, specify that, a heuristic search chooses that node to expand at any point in the search. By proper definition, a heuristic search is not guaranteed to improve performance for a particular problem or set of problems, but they are implemented in the hope of either improving the speed of that a solution is found and or the quality of the solution found. However, we may be able to find optimal solutions that are as good as possible with respect to some measure.
How AI think rationally& acting rationally
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