FSA Lab Exercises Week 2

> module FSAlab2
> where 
> import Data.List

You all know Mastermind. If you have to refresh your mind on the rules of the game, please consult wikipedia.

Generalized mastermind, played with \(n\) colours and \(p\) pegs, is NP Complete (Bondt 2004).

(Eijck and Unger 2010) gives an implementation of Mastermind where the computer picks the secret code, the user is supposed to guess the code on the basis of the feedback about the number of pegs in the correct position and the number of pegs of the right colour but in the wrong positions.

Here are some datatypes for this:

> data Colour   = Red | Yellow | Blue | Green | Orange 
>                 deriving (Eq,Show,Bounded,Enum)
> data Answer   = Black | White deriving (Eq,Show)
> type Pattern  = [Colour]
> type Feedback = [Answer]

Comparing two patterns and compute a reaction:

> samepos :: Pattern -> Pattern -> Int
> samepos _      []                 = 0 
> samepos []     _                  = 0 
> samepos (x:xs) (y:ys) | x == y    = samepos xs ys + 1
>                       | otherwise = samepos xs ys 
> occurscount ::  Pattern -> Pattern -> Int
> occurscount xs []       = 0
> occurscount xs (y:ys) 
>           | y `elem` xs = occurscount (delete y xs) ys + 1
>           | otherwise   = occurscount xs ys 
> reaction :: Pattern -> Pattern -> [Answer]
> reaction secret guess = take n (repeat Black) 
>                      ++ take m (repeat White)
>    where n = samepos secret guess 
>          m = occurscount secret guess - n 

Here the exercise is to give an implementation of Mastermind where the user picks the secret code and the computer makes the guesses, on the basis of the feedback of the user.

A naive algorithm for this is: keep a list of all codes that are not ruled out by the feedback to the guesses that were made so far. Make the first item on this list your next guess.

Exercise 1

Implement Mastermind using this algorithm.

In (Knuth 1976–1977) a different algorithm is proposed.

Let code be the set of currently possible codes. For each possible code \(x\), compute the partition of code according to the possible answers \((n,m)\) that \(x\) gives for the members \(y\) of code (candidates for the correct code). Pick an \(x\) that minimizes the number of elements in the largest \(V_i\) for the next guess.

Exercise 2

Implement Mastermind using this algorithm. (If the above description is not clear enough, consult (Knuth 1976–1977).)

In (Kooi 2005) another strategy is proposed. Instead of picking a next guess by minimizing the size of the largest partition block for all the possible answers that it could generate on the currently possible code store, pick a next guess that maximizes the number of partition blocks.

Exercise 3

Implement Mastermind using this algorithm. (Again, if the above description is not clear enough, consult (Kooi 2005).)

(Kooi 2005) discusses some other strategies, too. One strategy computes, instead of the worst case of the largest partition block, the expected size of the generated partition elements: the probability of getting the answer corresponding to a partition element times the size of that partition element.

Exercise 4

Implement Mastermind using this algorithm. This is a modification of the Knuth worst case algorithm.

Yet another strategy discussed by (Kooi 2005) is based on information theory. At each stage in the guessing, compute an answer that maximizes the entropy. However, the author remarks that this algorithm performs badly when compared with all non-naive alternatives. Quote from (Kooi 2005):

A possible explanation is that when one calculates the entropy, 
the base of the logarithm is important when one compares partitions
that have a different number of elements. When one compares partitions
of the same size, entropy is a good measure, otherwise it is not so good. 
Perhaps another new strategy could be based on taking entropy where the 
base of the logarithm depends on the size of the partition. 

Exercise 5

Analyze this, and see if you can come up with a correct version of the maximize entropy strategy.

The Balance puzzle that was discussed in class is another example of an information feedback game. We discussed the balance problem for 12 coins and one counterfeit coin that could be either lighter or heavier than the normal coins. This can be generalized by allowing \(n\) coins, with either \(d\) out of \(n\) different, or \(l\) out of \(n\) lighter, or \(h\) out of \(n\) heavier. Each choice \((n,d)\), \((n,l)\) or \((n,h)\) gives a different game.

The similarities with Mastermind are:

In order to turn Balance into an interesting game, make some assumptions. The following seem reasonable: normal coins all have the same weight, heavy coins all have the same weight, light coins all have the same weight.

Instead of this one could assume that heavy coins all exceed the weight of a normal coin by a different amount, and similarly for light coins. But this makes the game much more difficult.

Exercise 6

Fix the rules of Balance in some reasonable way (see above), invent a strategy for Balance and implement it. Next, analyze your strategy. Can you use the rule maximize the entropy of each balancing act? How?

Note These exercises are open-ended. Don't worry if you do not manage to carry out all of them before the deadline.

New submission deadline is Monday, September 19th, at midnight.

Bondt, Michiel de. 2004. “NP-Completeness of Master Mind and Minesweeper.” 0418. Department of Mathematics, Radboud University Nijmegen.

Eijck, Jan van, and Christina Unger. 2010. Computational Semantics with Functional Programming. Cambridge University Press.

Knuth, D. 1976–1977. “The Computer as Master Mind.” Journal of Recreational Mathematics 9 (1): 1–6.

Kooi, Barteld. 2005. “Yet Another Mastermind Strategy.” ICGA Journal 28 (1): 13–20.