Mastermind minimax algorithm

问题

I am trying to implement in python Donald Knuth's algorithm for codebreaking mastermind in not more than 5 moves. I have checked my code several times, and it seems to follow the algorithm, as its stated here: http://en.wikipedia.org/wiki/Mastermind_(board_game)#Five-guess_algorithm

However, I get that some of the secrets take 7 or even 8 moves to accomplish. Here is the code:

#returns how many bulls and cows there are
def HowManyBc(guess,secret):
    invalid=max(guess)+1
    bulls=0
    cows=0
    r=0
    while r<4:
        if guess[r]==secret[r]:
            bulls=bulls+1
            secret[r]=invalid
            guess[r]=invalid
        r=r+1
    r=0
    while r<4:
        p=0
        while p<4:
            if guess[r]==secret[p] and guess[r]!=invalid:
                cows=cows+1
                secret[p]=invalid
                break
            p=p+1
        r=r+1    
    return [bulls,cows]

# sends every BC to its index in HMList
def Adjustment(BC1):
    if BC1==[0,0]:
        return 0
    elif BC1==[0,1]:
        return 1
    elif BC1==[0,2]:
        return 2
    elif BC1==[0,3]:
       return 3
    elif BC1==[0,4]:
        return 4
    elif BC1==[1,0]:
        return 5
    elif BC1==[1,1]:
        return 6
    elif BC1==[1,2]:
        return 7
    elif BC1==[1,3]:
        return 8
    elif BC1==[2,0]:
        return 9
    elif BC1==[2,1]:
        return 10
    elif BC1==[2,2]:
        return 11
    elif BC1==[3,0]:
        return 12
    elif BC1==[4,0]:
    return 13
# sends every index in HMList to its BC
def AdjustmentInverse(place):
    if place==0:
        return [0,0]
    elif place==1:
        return [0,1]
    elif place==2:
        return [0,2]
    elif place==3:
        return [0,3]
    elif place==4:
        return [0,4]
    elif place==5:
        return [1,0]
    elif place==6:
        return [1,1]
    elif place==7:
        return [1,2]
    elif place==8:
        return [1,3]
    elif place==9:
        return [2,0]
    elif place==10:
        return [2,1]
    elif place==11:
        return [2,2]
    elif place==12:
        return [3,0]
    elif place==13:
        return [4,0]   
# gives minimum of positive list without including its zeros    
def MinimumNozeros(List1):
    minimum=max(List1)+1
    for item in List1:
        if item!=0 and item<minimum:
            minimum=item
    return minimum

#list with all possible guesses
allList=[]
for i0 in range(0,6):
    for i1 in range(0,6):
        for i2 in range(0,6):
            for i3 in range(0,6):
                allList.append([i0,i1,i2,i3])
TempList=[[0,0,5,4]]
for secret in TempList:
    guess=[0,0,1,1]
    BC=HowManyBc(guess[:],secret[:])
    counter=1
    optionList=[]
    for i0 in range(0,6):
        for i1 in range(0,6):
            for i2 in range(0,6):
                for i3 in range(0,6):
                    optionList.append([i0,i1,i2,i3])
    while BC!=[4,0]:
        dummyList=[] #list with possible secrets for this guess
        for i0 in range(0,6):
            for i1 in range(0,6):
                for i2 in range(0,6):
                    for i3 in range(0,6):
                        opSecret=[i0,i1,i2,i3]
                        if HowManyBc(guess[:],opSecret[:])==BC:
                            dummyList.append(opSecret)
        List1=[item for item in optionList if item in dummyList]
        optionList=List1[:] # intersection of optionList and dummyList
        item1Max=0
        for item1 in allList:
            ListBC=[] # [list of all [bulls,cows] in optionList
            for item2 in optionList:
                ListBC.append(HowManyBc(item1[:],item2[:]))
            HMList=[0]*14 # counts how many B/C there are for item2 in optionList
            for BC1 in ListBC:
                index=Adjustment(BC1)
                HMList[index]=HMList[index]+1
            m=max(HMList)#maximum [B,C], more left - less eliminated (min in minimax)
            maxList=[i for i, j in enumerate(HMList) if j == m]
            maxElement=maxList[0] #index with max
            possibleGuess=[]
            if m>item1Max: #max of the guesses, the max in minimax
                item1Max=m
                possibleGuess=[i[:] for i in optionList if   AdjustmentInverse(maxElement)==HowManyBc(item1[:],i[:])]
                nextGuess=possibleGuess[0][:]
        guess=nextGuess[:]
        BC=HowManyBc(guess[:],secret[:])
        counter=counter+1

I get:

for [5, 3, 3, 4] counter is 7

for [5, 4, 4, 5] counter is 8

If someone could help I would appreciate it very much!

Thanks,mike

回答1:

1. What's wrong with your implementation

There are four mistakes.

The comment is wrong on this line:
```
m=max(HMList)#maximum [B,C], more left - less eliminated (min in minimax)
```
This is actually the "max" in the minimax (which should have been clear from the call to max). You are trying to find the guess that minimizes the maximum size of the groups of possible secrets that yield the same evaluation. Here we are finding the maximum size of the groups, so that's the "max".
That mistake caused you to make this one:
```
if m>item1Max: #max of the guesses, the max in minimax
```
Here you need to take the min, not the max.
On the following lines you pick the first item among optionList that would generate the same evaluation as item1:
```
possibleGuess=[i[:] for i in optionList if   AdjustmentInverse(maxElement)==HowManyBc(item1[:],i[:])]
nextGuess=possibleGuess[0][:]
```
But that's not right: the guess we want here is item1, not some other guess that would generate the same evaluation!
Finally, you don't properly handle the case where optionList has only one remaining item. In this case all possible guesses are equally good at distinguishing among this item, so the minimax procedure doesn't differentiate between the guesses. In this case you should just guess optionList[0].

2. Other comments on your code

The variable names are poorly chosen. For example, what is item1? This is the guess that you are evaluating, so surely it should be called something like possible_guess? I suspect that your mistake §1.3 above was partly caused by this poor choice of variable name.
There's vast amounts of needless copying. All of your [:] are pointless and can be removed. The variable List1 is also pointless (why not just assign to optionList?), as is nextGuess (which not just assign to guess?)
You build dummyList consisting of all possible secrets that would match the last guess, but then you throw away all the entries in dummyList that aren't also in optionList. So why not just loop over optionList and keep the entries that match? Like this:
```
optionList = [item for item in optionList if HowManyBc(guess, item)==BC]
```
You build up a table HMList which counts the number of occurrences of each pattern of bulls and cows. You have noted the fact that there are 14 possible (bull, cow) pairs and so you've written the functions Adjustment and AdjustmentInverse to map back and forth between (bull, cow) pairs and their indices in the list.

These functions could have much simpler implementations if you took a data-driven approach and used the built-in list.index method:
```
# Note that (3, 1) is not possible.
EVALUATIONS = [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (1, 0), (1, 1),
               (1, 2), (1, 3), (2, 0), (2, 1), (2, 2), (3, 0), (4, 0)]

def Adjustment(evaluation):
    return EVALUATIONS.index(evaluation)

def AdjustmentInverse(index):
    return EVALUATIONS[index]
```
But after fixing mistake §1.3 above, you don't need AdjustmentInverse any more. And Adjustment could be avoided if you kept the counts in a collections.Counter instead of a list. So instead of:
```
HMList=[0]*14 # counts how many B/C there are for item2 in optionList
for BC1 in ListBC:
    index=Adjustment(BC1)
    HMList[index]=HMList[index]+1
m=max(HMList)
```
you could simply write:
```
m = max(Counter(ListBC).values())
```

3. Improved code

Evaluating a guess (your function HowManyBc) can be reduced to just three lines using the class collections.Counter from the standard library.

from collections import Counter

def evaluate(guess, secret):
    """Return the pair (bulls, cows) where `bulls` is a count of the
    characters in `guess` that appear at the same position in `secret`
    and `cows` is a count of the characters in `guess` that appear at
    a different position in `secret`.

        >>> evaluate('ABCD', 'ACAD')
        (2, 1)
        >>> evaluate('ABAB', 'AABB')
        (2, 2)
        >>> evaluate('ABCD', 'DCBA')
        (0, 4)

    """
    matches = sum((Counter(secret) & Counter(guess)).values())
    bulls = sum(c == g for c, g in zip(secret, guess))
    return bulls, matches - bulls

I happen to prefer using letters for the codes in Mastermind. ACDB is so much nicer to read and type than [0, 2, 3, 1]. But my evaluate function is flexible as to how you represent the codes and guesses, as long as you represent them as sequences of comparable items, so you can use lists of numbers if you prefer.

Notice also that I've written some doctests: these are a quick way to simultaneously provide examples in the documentation and to test the function.

The function itertools.product provides a convenient way to build the list of codes without having to write four nested loops:
```
from itertools import product
ALL_CODES = [''.join(c) for c in product('ABCDEF', repeat=4)]
```

Knuth's five-guess algorithm uses the minimax principle. So why not implement it by taking the min of a sequence of calls to max?

def knuth(secret):
    """Run Knuth's 5-guess algorithm on the given secret."""
    assert(secret in ALL_CODES)
    codes = ALL_CODES
    key = lambda g: max(Counter(evaluate(g, c) for c in codes).values())
    guess = 'AABB'
    while True:
        feedback = evaluate(guess, secret)
        print("Guess {}: feedback {}".format(guess, feedback))
        if guess == secret:
            break
        codes = [c for c in codes if evaluate(guess, c) == feedback]
        if len(codes) == 1:
            guess = codes[0]
        else:
            guess = min(ALL_CODES, key=key)

Here's an example run:

>>> knuth('FEDA')
Guess AABB: feedback (0, 1)
Guess BCDD: feedback (1, 0)
Guess AEAC: feedback (1, 1)
Guess AFCC: feedback (0, 2)
Guess FEDA: feedback (4, 0)

来源：https://stackoverflow.com/questions/20298190/mastermind-minimax-algorithm

标签

python

algorithm

minimax