Working of Alpha-Beta Pruning in AI

Working of Alpha-Beta Pruning in AI

As we know, while working with AI and ML models we have faced one of the most important parameters which is that these models have taken too many times to process. The reason behind all this is because these have too many inputs and outputs already involved, the data set is very large and processing each and every data takes time and computational power. In order to cut short these expenses, there is a technique which is known as pruning. Pruning is a technique with the help of which the agent cut the extra input and output, or one can say least important parts can be cut off and hence the extra computational power can use elsewhere with the help of which lesser time and power can be consumed and we can get results and training of the model more quickly and efficiently. In AI terms pruning is a term with the help of which we can remove the leaf nodes which can no longer be used or are important for the system or the model. In this way, the prediction accuracy enhances, and better results can be obtained. In machine learning, pruning is a compression technique with the help of which the size of the decision trees can be shortened using algorithms. In machine learning, even the non-critical trees can be eliminated so that it can classify instances.

 

Alpha-Beta Pruning

Alpha-beta pruning is the most common type of pruning used in AI models. It is basically a kind of search algorithm with the help of which the AI model tends to decrease the least important nodes or the nodes that neither help nor harm in searching. In this way, the extra work or in AI terms extra computational force required by the model to process the results can be minimized and the model can generate results based on the prediction more effectively and efficiently. This search algorithm is known as alpha-beta pruning, the reason is that this algorithm is most commonly observed in two players’ games. The alpha agent helps to keep an update on the max’s player value while the beta helps to update the min player value. In normal searching, the whole value of the node is passed on to the next child node but here in alpha-beta pruning only the alpha, and beta values have passed to the childish nodes and all the other values have been sent back to the upper nodes, hence this doesn’t put the extra burden on the GPU or CPU of the model.

 

Working of Alpha-Beta Pruning

There is an article that presents the simplest working of alpha-beta pruning. The literature stated that it is an optimization technique with the help of which the searches become optimized with lesser computational time and power. With the help of the alpha-beta pruning or min-max algorithm, the model can search faster and with this high speed, the model can go deeper into new levels of the game tree. The main aim of this search algorithm is used in game theory, that it has always a better move by combining alpha and beta parameters. The purpose of these two parameters is as follows. 

  • Alpha is always the best value in the higher order to the maximizer. In this way, the maximizer can go to a higher level.
  • Beta is always the best value in the lower order to the minimizer. In this way, the minimizer can go to a higher level.

 

Advantages of Alpha-Beta Pruning

There are different advantages of search algorithms. In this way, alpha-beta pruning also has many advantages. This algorithm is mostly used in in-game theories and where searching is the first priority. It has different features that outscored other searching algorithms in this field. The few advantages of this algorithm are as follows. 

  • Alpha-beta pruning saves time, as the least used or least redundant nodes in the searching have been removed or reduced so that is the reason better time consumption can occur with the help of alpha-beta pruning.
  • When there are lesser nodes in searching the AI or ML agent requires lesser power for searching. So lesser computational power is required when an AI model uses an alpha-beta pruning algorithm as its search criteria.
  • As the least used branches and nodes are reduced from the searching, then the model or the system has more time, power, and capacity. So, with these, it can go for better deep searching, unleashing the deeper nodes better as compared to other conventional algorithms.
  • In conventional searching techniques, there are two agents required for searching like in the min-max algorithm. Here in the alpha-beta algorithm one agent is required for each and both of them to work parallelly and hence lesser time has been consumed.

 

Disadvantages of Alpha-Beta Pruning

There are also some disadvantages associated with alpha-beta pruning as well. That is the reason that different algorithms are also available in the market which is used by the developers. Some of these disadvantages are stated as follows. 

  • The main problem associated with alpha-beta pruning that the developers have pointed out is that this algorithm has not the capability to solve those problems which are associated with the conventional search algorithm named a min-max algorithm.
  • The searching associated with alpha-beta pruning required a depth limit, as it searches to the last node or branch of the tree. In this way, some extra computational power may have been consumed.
  • It not only calculates the good move’s time but also calculates the value of all the legal moves. In this way, a lot of time has been consumed.

 

Algoscale is one of the top artificial intelligence solution service providers and data analytics firms in the USA, offering top-tier services and product engineering to start-ups and Fortune 100 firms with a focus on ISVs, media publications, and retail service providers.

 

Recent Posts