In this course we will understand how artificial intelligence algorithms work. We will start by looking at traditional graph traversal algorithms i.e. breath first search and depth first search. From there will build our understanding of making searches more intelligent. Specifically, we will be looking at uniform cost search where we shall introduce weights to the edges and then shall modify this algorithm by applying estimates to it. We shall also look at hill climbing algorithm which is has a different perspective of searching the desired goal.

We will then look at how we can apply the concepts learnt into a new perspective of problem solving where if we have multiple available solutions then the algorithm should be able to find out which among them is the best solution.

We shall look at game playing where we will see show zero sum games work and how we can optimize these games by pruning a partial game tree. We shall first look at the working of a procedure called max-min algorithm and then in order to optimize this procedure, we shall apply pruning to it. We will look at what is shallow pruning and deep pruning and how take affect the game tree.