Sunday, February 28, 2010

Iterative deepening

Iterative deepening is a recursive procedure in which, we start searching to a fixed depth in the game tree. We apply the single evaluation function to the first branch, applying to it the result of possible moves. This will result in the initiation of a new minimax search, this time the depth getting increased to 2.this process gets repeated.

Thursday, February 25, 2010

Additional refinements

hi to all my dear friends!Waiting for quiescence is a technique in which we continue with the search unless no drastic change occurs from one level to another. The technique that searches a single chosen branch an additional two levels to make sure that it looks good is called secondary search. The performance of a program can often be notably increased if it is provided with a list of moves that can be made. 

Friday, February 19, 2010

Adding alpha –beta cutoffs

The alpha-beta cutoff strategy required the maintenance of two threshold values, one that represents a lower bound on the value that a maximizing node may have and another that represents an upper found on the value that a minimizing node may be assigned.

2. Else, generate one more branch for the tree by the function GEN (position, player) and setting SUCCESSOR to its returned list.

3. If SUCCESSOR is empty, then there are no moves to be made, so return back to DEEP the same structure that you would have returned if SUCCESSOR had returned true.

4. Else, if SUCCESSOR is not empty, examine each element and keep track of the best one.

Tuesday, February 16, 2010

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Thursday, February 11, 2010

Minimax search procedure

The Minimax search procedure is of the depth-first search procedure type. The basic idea behind this procedure is to start at the current position and then use the plausible-move generator to produce the set of feasible successor positions. We then apply the static evaluation function to these positions and choose the best among them. We then back this value to the starting positions is as good as our next position. The basic assumption for all these is that our evaluation function will return a maximum value because that our goal.

Monday, February 8, 2010

Static evaluation function

Hi to all my dear friends! Static evaluation function uses the information available on individual positions to estimate how they would eventually lead to a winning position.

Credit assignment problem is the problem in which we decide upon on the actions that were responsible for the present outcome. Let us see about this.

Tuesday, February 2, 2010

Game playing-artificial intelligence

Hi to all my dear friends! In this session, we will discuss techniques that makes game playing an interesting domain for AI programs. Two reasons could be attributed for such a line of thought:

1. They provide an easy way for measuring success or failure.

2. They do not require a great deal of knowledge to arrive at the winning position.