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我不知道如何将这些功能结合在一起以形成一个硬人工智能，它永远不会输。我应该以某种形式或方式使用递归，并且这些函数名称和合同是预先编写的，我填写了实际定义。后来用谷歌搜索了很多，我找不到任何相关的东西。有什么想法吗？

"""


State S2 is a *successor* of state S1 if S2 can be the the
next state after S1 in a legal game of tic tac toe.

safe: state -> Bool
successor: state x state -> Bool

1. If S is over, then S is safe if 'x' does not have 3 in a row in S.
2. If it is o's move in S, then S is safe iff at least one successor of S is safe.
3. If it is x's move in S, then S is safe iff all successors of S are safe.

A *stateList* is a list of states. 
"""


# safe: state-> Bool
#
# A state S is *safe* if player 'o' can force a win or tie from S.

def safe(S):
    if over(S): return not threeInRow('x',S)
    if turn(S)=='o': return someSafeSuccessor(S)
    if turn(S)=='x': return allSafeSuccessors(S)

def threeInRow(p,S):
    if p == 'x':
        if all(t in S[0] for t in (1,2,3)):
            return True
        elif all(t in S[0] for t in (4,5,6)):
            return True
        elif all(t in S[0] for t in (7,8,9)):
            return True
        elif all(t in S[0] for t in (1,4,7)):
            return True
        elif all(t in S[0] for t in (2,5,8)):
            return True
        elif all(t in S[0] for t in (3,6,9)):
            return True
        elif all(t in S[0] for t in (1,5,9)):
            return True
        elif all(t in S[0] for t in (3,5,7)):
            return True
    else:
        if all(t in S[1] for t in (1,2,3)):
            return True
        elif all(t in S[1] for t in (4,5,6)):
            return True
        elif all(t in S[1] for t in (7,8,9)):
            return True
        elif all(t in S[1] for t in (1,4,7)):
            return True
        elif all(t in S[1] for t in (2,5,8)):
            return True
        elif all(t in S[1] for t in (3,6,9)):
            return True
        elif all(t in S[1] for t in (1,5,9)):
            return True
        elif all(t in S[1] for t in (3,5,7)):
            return True

# someSafeSuccessor: state -> Bool
#
# If S is a state, someSafeSuccessor(S) means that S has
# at least one safe successor.

def someSafeSuccessor(S):
    flag = False
    # flag means we have found a safe successor
    for x in successors(S):
        if safe(x): flag = True
    return flag

# allSafeSuccessors: state -> Bool
#
# If S is a state, allSafeSuccessors(S) means that every
# successor of S is safe.
def allSafeSuccessors(S):
  flag = True
  for x in successors(S):
    if not safe(x): flag = False
  return flag    


# successors: state -> stateList
#
# successors(S) is a list whose members are all of the successors of S.
def successors(S):
  stateList=[]
  for i in range(1,10):
    if empty(i,S):
      stateList.extend(S[0],S[1]+[i])
  return stateList
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1 回答 1

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跟进我的评论。

在描述 minimax（/alpha-beta 修剪）算法时可视化的树不是“真正的树”，因为您在内存中构建了整个树。它是一棵概念树，首先测试每个移动深度的结果，记下每个叶子（alpha、beta 等）的分数并将它们向上传播。

注意这句话，深度优先。这意味着您的递归 minimax-implementing 函数首先调用它自己可以做的第一步。首先是它可以采取的第一步调用自己。依此类推，直到达到最大深度或最终移动，然后返回。您可以通过这个逻辑看到，除了现在正在考虑的单个移动链之外，您在内存或任何外部存储中永远不会有更多的板（并且，在每个级别，您将遍历可能的移动列表）它 - 所以还有关于你有多远的记忆等）。

tl;dr 通过进行深度优先的极小极大递归，除了单个递归函数之外，您不会创建任何新函数。

于 2013-04-18T05:26:00.967 回答