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# The Most Expensive Peace Army

#### Author(s): Optimization team

Originally published on Towards AI.

It’s widely acknowledged that on a standard chessboard, precisely 8 queens, 8 rooks, 14 bishops, 32 knights, or 16 kings can be arranged without threatening each other. But what if we amalgamated these pieces?

Let’s attribute fractional values: 1/8 for a queen (Q), 1/8 for a rook (R), 1/14 for a bishop (B), 1/32 for a knight (N), and 1/16 for a king (K). Now, let’s strategize to create the most valuable chess army possible on a chessboard, employing any combination of these pieces while ensuring they coexist peacefully without posing a threat to one another.

## Problem formulation:

before we formulate the problem, we should start by the definition of neighborhood for each chess piece

## King

Any cell with a distance less than sqrt(2)

## Queen

Let’s find the neibours of cell i (x0,y0)

cell j is neghbour of cell i if it has any of these properties:

• X=x0
• Y=y0
• U+007CX-x0U+007C=U+007CY-y0U+007C

## Knight

cell j is neghbour of cell i if it has any of these properties:

• (X-x0,Y-y0) in this list
`[ (1,2),(1,-2) , (-1,2), (-1,-2) , (2,1), (2,-1) , (-2,-1),(-2,1) ]`

## Bishop

cell j is neghbour of cell i if it has any of these properties:

• U+007CX-x0U+007C=U+007CY-y0U+007C

## Rook

cell j is neghbour of cell i if it has any of these properties:

• X=x0
• Y=y0

Now let’s talk about the math model:

1. OF is trying to maximize the value of the army (Vp is the value of piece p)
2. For each piece (p): we need to have at least one cell assigned to it.
3. For each cell (n): not more than one piece can sit in it
4. if a piece is assigned to cell n then this cell is occupied
5. If a cell is occupied then there should be no other peice threatening this cell

## Python Code:

`def SolveWithTimeLimitSampleSat(): model = cp_model.CpModel() # Creates the variables. pieces = ['K', 'R', 'Q', 'N', 'B' ] val= {'K':14, 'R':28, 'Q':28, 'N':7, 'B':16 } symb= {'K':"\u265A", 'R':"\u265C", 'Q':"\u265B", 'N':"\u265E", 'B':"\u265D" } U = {(n,p):model.NewBoolVar(f'assign_{n}_{p}') for n in nodes for p in pieces} Occupied = {n:model.NewBoolVar(f'O_{n}') for n in nodes}  for p in pieces: expressions = [U[n,p] for n in nodes] model.AddAtLeastOne(expressions) for n in nodes:  expressions = [U[n,p] for p in pieces] model.Add(sum(expressions) == Occupied[n]) model.AddAtMostOne(expressions) expressions_king_around = [U[counter,'K'] for counter in nodes if counter in Nking[n] ] expressions_rook_around = [U[counter,'R'] for counter in nodes if counter in NR[n] ] expressions_queen_around = [U[counter,'Q'] for counter in nodes if counter in NQ[n] ] expressions_knight_around = [U[counter,'N'] for counter in nodes if counter in Nknight[n] ] expressions_bishop_around = [U[counter,'B'] for counter in nodes if counter in NB[n] ]  around = expressions_king_around + expressions_rook_around + expressions_queen_around + expressions_knight_around + expressions_bishop_around model.Add( sum(around) == 0 ).OnlyEnforceIf(Occupied[n]) expressions_of = [val[p]*v for (n,p),v in U.items() ] model.Maximize(sum(expressions_of)) solver = cp_model.CpSolver() solver.parameters.max_time_in_seconds = 30.0 status = solver.Solve(model) if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE: print('OF = ', status, solver.ObjectiveValue())`

## Observations:

• The problem has symmetry and this makes it hard for solver to solve.
• There might be more than one solution
• The code is written using Constraint Programming Tool (ORTools) but the formulation is in MILP format.

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Published via Towards AI