Summary - Game theory and parallel

Print the minimax value in a mini task.

Summary - Game theory and parallel

In this group we covered game theory and parallel algorithms: minimax, alpha-beta pruning, Nash equilibrium, parallel algorithms and MapReduce.

Minimax and alpha-beta make optimal decisions in game trees. Nash equilibrium defines balance in strategic interaction. Parallel algorithms and MapReduce process big data quickly.

We will now revisit minimax with a small task.

Exercise minimax mini task

Write minimax(node, is_max). The max player picks the largest, the min player the smallest. Print 2 with print(minimax([[1, 4], [2, 3]], True)).

Show example solution

def minimax(node, is_max):
    # Simple minimax value
    if not isinstance(node, list):
        return node
    if is_max:
        return max(minimax(child, False) for child in node)
    return min(minimax(child, True) for child in node)
print(minimax([[1, 4], [2, 3]], True))

Test yourself

Which method finds the optimal move in a game tree?
- Greedy
- BFS
- Insertion sort
- Minimax
Show answer

Minimax — Minimax gives the optimal move in a game tree.

minimax value

# node = [[1, 4], [2, 3]], is_max = True
# minimax(node, True) = ____

Show answer

# node = [[1, 4], [2, 3]], is_max = True
# minimax(node, True) = 2

Method-purpose

minimax

Game tree
alpha-beta

Pruning
Nash

Equilibrium
MapReduce

Big data

Next lesson: Challenge 1

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