Search algorithms form the core of such Artificial Intelligence programs. And while we may be inclined to think that this has limited applicability only in areas of gaming and puzzle-solving, such algorithms are in fact used in many more AI areas like route and cost optimizations, action planning, knowledge mining, robotics, autonomous driving, computational biology, software and hardware verification, theorem proving etc.

If we consider searching as a form of traversal in a graph, an uninformed search algorithm would blindly traverse to the next node in a given manner without considering the cost associated with that step. An informed search, like Best first search, on the other hand, would use an evaluation function to decide which among the various available nodes is the most promising (or ‘BEST’) before traversing to that node.

The Best first search uses the concept of a Priority queue and heuristic search. To search the graph space, the BFS method uses two lists for tracking the traversal. An ‘Open’ list which keeps track of the current ‘immediate’ nodes available for traversal and ‘CLOSED’ list that keeps track of the nodes already traversed.

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