1. Find Solution of game theory problem using matrix method 
| Player A\Player B | B1 | B2 | B3 | 
| A1 | 1 | 7 | 2 | 
| A2 | 6 | 2 | 7 | 
| A3 | 5 | 1 | 6 | 
Solution:1. Saddle point testing
Players
|  |  |  | Player `B` |  |  | 
|  |  |  | `B_1` | `B_2` | `B_3` |  |  | 
| Player `A` | `A_1` |  | 1 | 7 | 2 |  | 
| `A_2` |  | 6 | 2 | 7 |  | 
| `A_3` |  | 5 | 1 | 6 |  | 
We apply the maximin (minimax) principle to analyze the game.
|  |  |  | Player `B` |  |  | 
|  |  |  | `B_1` | `B_2` | `B_3` |  | Row Minimum
 | 
| Player `A` | `A_1` |  | 1 | 7 | 2 |  | `1` | 
| `A_2` |  | (6) | [2] | 7 |  | `[2]` | 
| `A_3` |  | 5 | 1 | 6 |  | `1` | 
|  | Column Maximum
 |  | `(6)` | `7` | `7` |  |  | 
Select minimum from the maximum of columns
Column MiniMax = (6)
Select maximum from the minimum of rows
Row MaxiMin = [2]
Here, Column MiniMax `!=` Row MaxiMin
`:.` This game has no saddle point.
2. Dominance rule to reduce the size of the payoff matrix
Using dominance property
|  |  |  | Player `B` |  |  | 
|  |  |  | `B_1` | `B_2` | `B_3` |  |  | 
| Player `A` | `A_1` |  | 1 | 7 | 2 |  | 
| `A_2` |  | 6 | 2 | 7 |  | 
| `A_3` |  | 5 | 1 | 6 |  | 
row-3 `<=` row-2, so remove row-3
|  |  |  | Player `B` |  |  | 
|  |  |  | `B_1` | `B_2` | `B_3` |  |  | 
| Player `A` | `A_1` |  | 1 | 7 | 2 |  | 
| `A_2` |  | 6 | 2 | 7 |  | 
column-3 `>=` column-1, so remove column-3
|  |  |  | Player `B` |  |  | 
|  |  |  | `B_1` | `B_2` |  |  | 
| Player `A` | `A_1` |  | 1 | 7 |  | 
| `A_2` |  | 6 | 2 |  | 
reduced matrix
|  |  |  | Player `B` |  |  | 
|  |  |  | `B_1` | `B_2` |  |  | 
| Player `A` | `A_1` |  | 1 | 7 |  | 
| `A_2` |  | 6 | 2 |  | 
For this reduced matrix, calculate `P_(Adj)` and `P_(Cof)`
`P_(Adj) = [[2,-7],[-6,1]]`
and `P_(Cof) = [[2,-6],[-7,1]]`
Player A's optimal strategies `=([[1,1]] xx P_(Adj))/([[1,1]] xx P_(Adj) xx [[1],[1]])`
`=([[1,1]][[2,-7],[-6,1]])/([[1,1]][[2,-7],[-6,1]][[1],[1]])`
`=([[-4,-6]])/(-10)`
`=[[2/5,3/5]]`
`p_1=2/5` and `p_2=3/5`, where `p_1` and `p_2` represent the probabilities of player A's, using his strategies `A_1` and `A_2` respectively.
Similarly, 
Player B's optimal strategies `=([[1,1]] xx P_(Cof))/([[1,1]] xx P_(Adj) xx [[1],[1]])`
`=([[1,1]][[2,-6],[-7,1]])/([[1,1]][[2,-7],[-6,1]][[1],[1]])`
`=([[-5,-5]])/(-10)`
`=[[1/2,1/2]]`
`q_1=1/2` and `q_2=1/2`, where `q_1` and `q_2` represent the probabilities of player B's, using his strategies `B_1` and `B_2` respectively.
Hence, Value of the game `V` = (Player A's optimal strategies) `xx` (Payoff matrix `P_(ij)`) `xx` (Player B's optimal strategies)
`V=[[2/5,3/5]][[1,7],[6,2]][[1/2],[1/2]]=4`