Summary - Game theory and parallel
- Algorithms course
- Expert
- Lesson 36/41
- 4 min
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.
Practice 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)).
Starter code
def minimax(node, is_max):
# Simple minimax valueSample 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))Expected output
2
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 Which method finds the optimal move in a game tree?
- Greedy
- BFS
- Insertion sort
- Minimax Correct answer
Explanation: Minimax gives the optimal move in a game tree.
2 minimax value
# node = [[1, 4], [2, 3]], is_max = True
# minimax(node, True) = ____# node = [[1, 4], [2, 3]], is_max = True
# minimax(node, True) = 2Explanation: max(min(1,4), min(2,3)) = max(1, 2) = 2.
3 Method-purpose
| Term | Meaning |
|---|---|
minimax |
Game tree |
alpha-beta |
Pruning |
Nash |
Equilibrium |
MapReduce |
Big data |