# What is Alpha Beta Pruning in Artificial Intelligence?

Last updated on October 15th, 2021 at 12:43 pm

What is Artificial Intelligence?

Most of us are aware of the edge cutting technology i.e. Artificial Intelligence (AI). It is used to create machines that have their decision-making capability. They can learn from their work environment and can behave autonomously. in the initial stages, it is man-made but once it has learned and evolved, it can enhance itself.

For example, the University of California, Irvine developed an AI machine that could solve Rubikâ€™s cube. The machine learns and trains itself through algorithms and now it can solve complicated Rubikâ€™s cube in a fraction of a second. In this article, let us learn about Alpha Beta Pruning in AI.

What is Alpha Beta Pruning?

Before you learn about Alpha-Beta Pruning, one needs to know about the minimax algorithm. Minimax algorithm backtracks a scenario/game and finds the best move which will enhance the decision making or in terms of gaming, will maximize the chances of winning. It assumes that there is an opponent who is also trying to win, it tries to reduce the winning chances of the opponent and optimizing its steps to win.

Alpha Beta Pruning is an optimization technique that decreases the number of steps in the minimax algorithm. It helps in reducing the number of steps in searching/traversing. For example, if we are applying a minimax algorithm in a chess game, then Alpha Beta Pruning helps in finding those steps which will not result in winning, and then those steps need not be traversed.

The minimax algorithm prepares a search tree after backtracking, there are many nodes in this search tree. The redundant/useless nodes are eradicated with the help of Alpha-Beta Pruning. It helps in decreasing complexity and saves time. There are two main components in the minimax algorithm, first one is maximizer which tries to get the highest score and the minimizer does the opposite. Let us know about the two parameters â€˜Alphaâ€™ & â€˜Betaâ€™:

Alpha Parameter (Î±):

The best choice/decision found in the whole path of maximizer is called Alpha. Its initial value is (-âˆž). One can also say that the highest value along the path of maximizer is Alpha.

Beta Parameter (Î²):

The best choice/decision found in the path of minimizer is called Beta. It is the lowest value of all the values encountered in the path of the minimizer. The initial value of the Beta parameter is supposed to be (+âˆž).

Â Note: Before Pruning one needs to check whether (Î±>=Î²). This is a necessary condition to run the Alpha Beta Pruning algorithm.

Why Alpha Beta Pruning is important?

There is no change in the result if we compare the outputs minimax algorithm and Alpha-Beta Pruning. Pruning helps in decreasing the number of steps thus making the algorithm faster and less complex.

Key points and terminologies in Alpha-Beta Pruning

1. The child node is provided with the values of Î± & Î².Â  While backtracking, the values of lower-order nodes are passed to the upper nodes in the search tree except for the child node.
2. In some cases, the Alpha Beta Pruning algorithm fails to reduce the number of nodes. In such cases, more time is wasted because of Î± & Î² parameters and the number of steps comes out to be the same as the minimax algorithm.
3. This scenario is called Worst Ordering. Ideal Ordering occurs when a lot of pruning happens and a lot of steps are decreased (especially on the left side of the tree).

Conclusion

AI is a budding technology and is expected to grow more. If you want to learn more about AI, then you can search for courses available online. One such good course is the PG program in Analytics & Artificial Intelligence in Imarticus Learningâ€™s list of offered programs. This article was all about the Alpha Beta Pruning algorithm in AI. I hope it helps!

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