Intelligent Agent to play Connect-4 with a modifiable depth. App also offers a GUI decision tree visualizer to trace the minimax algorithm execution.
- Smart Connect4
- The project was built using Python 3.9, make sure you configure your python interpreter correctly
- You should run the "interface.py" python file, you can do that by running the following command inside the project folder
python interface.py-
The simplest way to represent the board is using a 2D array. However, using 2D array uses (
$6\times 7\times size of integer$ ). This representation would limits the agent by only reaching short search depth and doesn't enable the agent to go deep in the search tree. -
The board configuration (state) is represented as 64-bit number as each column takes 9 bits and is represented as follows:
- A 3 bits for each column which points to the last empty location, we can call them last location mask, for example if the three bits is "001" indeicates that the next location is the first row
- A 6 bits representing each slot inside that column, 0 for the human and 1 for the intelligent agent.
- The 64th bit indicates whether the state is pruned or not, it is only useful when printing the search tree so we can neglect it in most of our operations (check the Alpha Beta Pruning section for a better understanding).
-
We can conclude from the previous representation that an empty board will be represented as the bits:
1 001000000 001000000 001000000 001000000 001000000 001000000 001000000
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Those bits are equal to the number "10378549747928563776" in decimal.
-
Actions are implemented by incrementing the last location mask of every column and upating the bit corresponding to that slot.
- MiniMax Algorihm Pseudocode:
Function MiniMax(maxDepth, currentDepth, isMaxPlayer, state) if currentDepth == maxDepth return state,heuristic(state) if isGameOver(state) return state,getFinalScore(state) children = getChilder(state) if isMaxPlayer // if it is the agent's turn Initialize maxChild, maxValue with negative infinity for child in children childValue = MiniMax(maxDepth,currentDepth+1, not isMaxPlayer, child)[1] if childValue > maxValue maxChild = child, maxValue = childValue return maxChild, maxValue else Initialize minChild, minValue with positive infinity for child in children childValue = MiniMax(maxDepth,currentDepth+1, not isMaxPlayer, child)[1] if childValue < minValue minChild = child, minValue = childValue return minChild, minValue
- Hash map to map from states to its children (next states).
- Hash map to map from states to its values.
- Since that branching factor in connect-4 is 7, and the depth is 42, then the total number of states to search is 7^42 which is nearly equals 3x10^35 state. The agent needs to use some pruning to enhance the runtime and work in real time.
- Heuristic pruning is a modified version of minimax algorithm used in games that has high branching factor of states and high depth.
- We simulate the game until certain depth (entered by the user) and then we calculate the heuristic value that estimates the power of current states compared to other states to choose the best choice according to the minimax algorithm
- Certain weighting criteria is developed by us to evaluate the strength of the moves.
- In this Program we offer 2 heuristics:
- The more intuitive and simple one:
- Positive part
- 4 consecutive (AI color) gets 4 points
- 3 candidate consecutive (AI color) gets 3 points
- 2 candidate consecutive (AI color) gets 2 points
- stopping opponent from getting a point gets 1 point
- Negative part
- 4 consecutive (Human color) gets -4 points
- 3 candidate consecutive (Human color) gets -3 points
- 2 candidate consecutive (Human color) gets -2 points
- stopping AI from getting a point gets -1 point
- Positive part
- The more aware heuristic :
- Positive part
- 4 consecutive (AI color) gets 40 points
- 3 candidate consecutive (AI color) gets 17 points (next move will gaurantee the point)
- 3 candidate consecutive (AI color) gets 15 points (a colomn is not build yet)
- 2 candidate consecutive (AI color) gets 4 points (next move will gaurantee the point)
- 2 candidate consecutive (AI color) gets 2 points (a colomn is not build yet)
- stopping opponent from getting a point gets 13 point
- Negative part
- 4 consecutive (Human color) gets -40 points
- 3 candidate consecutive (Human color) gets -17 points (next move will gaurantee the point)
- 3 candidate consecutive (Human color) gets -15 points (a colomn is not build yet)
- 2 candidate consecutive (Human color) gets -4 points (next move will gaurantee the point)
- 2 candidate consecutive (Human color) gets -2 points (a colomn is not build yet)
- stopping AI from getting a point gets -13 point
- Positive part
- Alpha-Beta pruning is a modified version of the minimax algorithm to optimize it.
- Alpha-Beta can be a real game changer, it cannot eliminate the exponent, but it can cuts it to half.
- Alpha-Beta Pruning can -without checking each node of the game tree- compute the correct minimax decision. This involves two threshold parameter Alpha and beta for future expansion, so it is called alpha-beta pruning.
- Alpha: The best (highest-value) choice we have found so far at any point along the path of Maximizer. The initial value of alpha is -∞.
- Beta: The best (lowest-value) choice we have found so far at any point along the path of Minimizer. The initial value of beta is +∞.
- The condition for the pruning is: α>=β
Alpha is only updated by Max Player, while Beta is only updated by Min player.
Function MiniMaxAlphaBeta(maxDepth, currentDepth, isMaxPlayer, state, alpha,beta)
if currentDepth == maxDepth
return state,heuristic(state)
if isGameOver(state)
return state,getFinalScore(state)
children = getChilder(state)
if isMaxPlayer // if it is the agent's turn
Initialize maxChild, maxValue with negative infinity
for child in children
childValue = MiniMax(maxDepth,currentDepth+1, not isMaxPlayer, child)[1]
if childValue > maxValue
maxChild = child, maxValue = childValue
if maxValue > alpha:
alpha = maxValue
if alpha >= beta:
break
return maxChild, maxValue
else
Initialize minChild, minValue with positive infinity
for child in children
childValue = MiniMax(maxDepth,currentDepth+1, not isMaxPlayer, child)[1]
if childValue < minValue
minChild = child, minValue = childValue
if minValue < beta:
beta = minValue
if beta <= alpha:
break
return minChild, minValue- A lot of research regarding the connect-4 game has shown that playing in the middle or near the middle enhances the chances of winning the game, playing in the middle is considered in a lot of time the best choice.
- Exploring a state where we place a piece in the middle firstly has enhanced the alpha-beta pruning quite well especially in early game.
- The following anlaysis is done on some random states, so the numbers can varies from one state to another in case of pruning.
- The time is measured in seconds.
| Depth | States Expanded | Time Taken |
|---|---|---|
| 1 | 8 | 0.001 |
| 2 | 57 | 0.007 |
| 3 | 400 | 0.069 |
| 4 | 2775 | 0.521 |
| 5 | 19608 | 3.01 |
| Depth | States Expanded | Time Taken |
|---|---|---|
| 1 | 8 | 0.001 |
| 2 | 27 | 0.005 |
| 3 | 84 | 0.013 |
| 4 | 235 | 0.115 |
| 5 | 1111 | 0.645 |
1- Yousef Kotp
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