MiniMax And Alpha Beta Pruning

Minimax : Minimax (sometimes minmax) is a decision rule used in decision theory, game  theory, statistics and philosophy for minimizing the possible loss for a worst  case (maximum loss) scenario. Alternatively, it can be thought of as maximizing  the minimum gain (maximin).


Problem 1 with Solution:

How it Work :


Step 1 : From 3,12,8 value 3 is the most MIN value so in first step it takes value 3 .


Step 2 : From 2,4,6 value 2 is the most MIN value so in first step it takes value 2 .


Step 3 : From 14,5,2 value 2 is the most MIN value so in first step it takes value 2 .


Step 2 : From 3,2,2 value 3 is the most MAX value so in first step it takes value 3 .


Problem 2 with Solution:

How it Work :


Step 1 : From 6,4,10 value 4 is the most MIN value so in first step it takes value 4 .


Step 2 : From 3,8,7 value 3 is the most MIN value so in first step it takes value 3 .


Step 3 : From 9,1,12 value 1 is the most MIN value so in first step it takes value 1 .


Step 2 : From 4,3,1 value 4 is the most MAX value so in first step it takes value 4 .




Alpha Beta Pruning :  In AB Pruning it is possible to ignore entire sections of the search space and come up with the same answer. If a line of play leads a worse position than another one that already discovered , then it is not necessary to explore that line anymore. 

Problem With Solution : 

I have found this video which is well defined for solving Alpha Beta Pruning Problem.