Artificial Intelligence by Omar Waleed

Artificial Intelligence )AI( attempts replicating the human ways of reasoning

in computing. As a full replication may not be approachable at once due to is magnitude

and complexity, research now targets commercialisable aspects of AI towards providing

“intelligent” assistive services to the human users [1]. Decision making in this paradigm

involves evaluating a number of alternatives in different spatial configurations, environments and circumstances and to find better of the alternatives. It also involves

decision making even when an ideal alternative is not derivable. This paper is therefore

limited to comparing the implementations of the popular AI algorithms, namely Breadth

First Search, Depth First Search, A*, Best First Search and Hill climbing algorithms for

solving a sliding n-puzzle in an attempt to look at the better efficient of the algorithms for

this case. To solve the sliding n puzzle problem, one moves a set of square tiles arranged

randomly in a square board to arrive at a pre-determined order. The board has only one

blank square and each tile can only move to the blank space adjacent to itself. Our aim in

this paper is to apply the AI approaches to the case and compare their performances in the

problem solving