Question: function Solution = SudokuNxN ( A , T ) % Get the size of the Sudoku grid gridSize = size ( A , 1 )

function Solution = SudokuNxN(A, T)
% Get the size of the Sudoku grid
gridSize = size(A,1);
boxSize = sqrt(gridSize); % Assumes the grid is a perfect square
% Initialize player existence and move arrays
players = zeros(gridSize, gridSize, 2);
for i =1:gridSize
for j =1:gridSize
if A(i, j)==0
players(i, j,1)=1; % Mark cell as active if it's empty
end
end
end
S =0;
N =1;
maxIterations =250000;
while S ==0 && N <= maxIterations
% Each player picks a move according to the learning rule
for i =1:gridSize
for j =1:gridSize
if players(i, j,1)==1
Test = A;
payoffs = zeros(1, gridSize);
for move =1:gridSize
Test(i, j)= move;
payoffs(move)=-0.5* MatchesNxN(Test, i, j, gridSize, boxSize);
end
players(i, j,2)= PickMove(payoffs, T);
end
end
end
% Update the matrix
for i =1:gridSize
for j =1:gridSize
if players(i, j,1)==1
A(i, j)= players(i, j,2);
end
end
end
T = T /1.00005; % Reduce the temperature
S = IsSolvedNxN(A, gridSize, boxSize); % Check if the puzzle is solved
N = N +1;
end
Solution = A;
if N == maxIterations +1
fprintf('Fail
');
elseif S ==1
fprintf('Resolved in %d iterations at temp %f
', N, T);
end
end
function M = MatchesNxN(A, i, j, gridSize, boxSize)
M =0;
% Check the row
for n =1:gridSize
if n ~= j && A(i, j)== A(i, n)
M = M +1;
end
end
% Check the column
for n =1:gridSize
if n ~= i && A(i, j)== A(n, j)
M = M +1;
end
end
% Check the sub-box
boxRow = floor((i-1)/boxSize)*boxSize +1;
boxCol = floor((j-1)/boxSize)*boxSize +1;
for n = boxRow:boxRow+boxSize-1
for m = boxCol:boxCol+boxSize-1
if ~(n == i && m == j) && A(i, j)== A(n, m)
M = M +1;
end
end
end
end
function S = IsSolvedNxN(A, gridSize, boxSize)
S =1; % Assume solved unless a conflict is found
for i =1:gridSize
for j =1:gridSize
if MatchesNxN(A, i, j, gridSize, boxSize)>0
S =0; % If any conflicts, mark as unsolved
return;
end
end
end
end
function move = PickMove(payoffs, T)
annealed = exp((1/T)* payoffs); % Calculate annealed probabilities
sumAnnealed = sum(annealed);
probabilities = annealed / sumAnnealed; % Normalize to create probability distribution
r = rand();
cumProb =0;
move =1; % Default move if none other selected
for k =1:length(payoffs)
cumProb = cumProb + probabilities(k);
if r <= cumProb
move = k;
break;
end
end
end
% Test with an example Sudoku puzzle and temperature
A =[
5300700000000000;
6001950000000000;
0980000600000000;
8000600030000000;
4008030010000000;
7000200060000000;
0600002800000000;
0004190050000000;
0000800790000000;
0000000000000000;
% Add more rows to complete the grid
];
T =10;
Solution = SudokuNxN(A, T);
disp(Solution);
Extend it to larger settings
Implement fictitious play in Matlab for rock-paper-scissors game, show that it converges to the equilibrium.
Then, extend it to rock-paper-scissors, superman, kryptonite game and demonstrate again that it converges to the equilibrium

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