Teaching Kids Programming: Videos on Data Structures and Algorithms
The Alpha Beta Pruning Optimisation Algorithm can also be applied directly on NegaMax – so that we can combine both players/scenarios instead of writing similar code twice.
The alpha, beta boundary when negated becomes (-beta, -alpha) – that is 
turns to 
The following is a Python code to implement the Alpha Beta Pruning Algorithm based on Negamax – with the Depth Limit Search aka the depth parameter is the max depth to search.
1 2 3 4 5 6 7 8 9 10 11 12 | def alphabeta(state, depth, player, alpha, beta): if d == 0 or isEnd(state): return score(state) score = -math.inf for act in actions(state): newState = succ(state, act) score = max(score, -alphabeta(newState, depth - 1, -player, -beta, -alpha) alpha = max(alpha, score) if alpha >= beta: break return score |
def alphabeta(state, depth, player, alpha, beta):
if d == 0 or isEnd(state):
return score(state)
score = -math.inf
for act in actions(state):
newState = succ(state, act)
score = max(score, -alphabeta(newState, depth - 1, -player, -beta, -alpha)
alpha = max(alpha, score)
if alpha >= beta:
break
return scoreTo have a higher chance of Alpha Beta pruning, we can order/sort the moves aka action from good moves to bad moves.
Game Theory – Game Algorithms
- Teaching Kids Programming – Alpha Beta Pruning Algorithm on NegaMax (Game Theory)
- Teaching Kids Programming – NegaMax Algorithm (Game Theory)
- Teaching Kids Programming – Alpha-Beta Pruning Algorithm (Game Theory)
- Teaching Kids Programming – Define Tic Tac Toe using Game Theory Terminologies
- Teaching Kids Programming – Introduction to Two Players Zero-Sum Game (Number Halving)
- Teaching Kids Programming – MinMax Algorithm in Game Tree (Game Theory, Tic Tac Toe)
–EOF (The Ultimate Computing & Technology Blog) —
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