What are the problems associated to Best First Search in Artificial intelligence?

Question

I Know general issues include local maxima and plateaus however I am curious if there are any more issues associated with this specific search and what my best course of action would be in order to overcome these issues.

Can someone also give me an example of which sort of problem this search would be good to use for?

Answer 1

Here are some problems that are related to Best-First-Search:

• Considers the cost to the goal from the current state

• Some paths can continue to look good according to the heuristic function, there is also an issue with an infinite branch. Assume you are following a branch where node at the depth I have a heuristic value of h(v_i) = 2^-i. You will never get to the zero value, but greedy best first will keep developing these nodes. You can see in the below diagram:

• It is greedy. In many cases it can lead to a very quick solution, however, it is usually not optimized, because your heuristic function has some error and sometimes gets the wrong answer which next node to explore.

There are many areas where you can use it:

• It is more efficient than that of BFS and DFS.

• The time complexity of the Best first search is much less than a Breadth-first search.

• The Best first search allows us to switch between paths by gaining the benefits of both the breadth-first and depth-first search. Because DFS is good a solution that can be found without computing all nodes and Breadth-first search is good because it does not get trapped in dead ends.

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