# Solving Knights Tour using a Neural Network

8 x 8 Knight's Tour generated by Neural Network

I recently came across this post by Dimitry Brant: Knight’s Tours Using a Neural Network. This version is implemented in C++ with flat arrays. A properly vectorized numpy re-implementation is long overdue, so I decided to give it a shot.

A knight’s tour is a sequence of moves of a knight on a chessboard such that the knight visits every square exactly once. There are at-least 4 ways to solve this: Backtracking, Divide an Conquer, Warnsdorff’s Heuristic and what most interesting of all, using a Neural Network.

This is neither the most efficient nor practical algorithm to solve this problem, but it’s definitely the most elegant way. Also, is it really a NN solution though? It feels more like a graph-theory problem to me.

## Algorithm

1. Setup a Knight’s Graph, A graph of all possible paths on a given chessboard. We represent this using a np array of shape (4, N). The 4 columns representing a Neuron’s links in the chess board<x0, y0, x1, y1>. N being the number of neurons. For an 8x8 chessboard N = 168.
2. Initialize Vt, a vector of size N, initialized randomly with 0/1. This vector is the Neural Network’s state. Each element in Vt represents one Neuron.
3. Initialize Ut, a zeros vector of size N.
4. Repeat following
1. Ut_1 = Ut + 3 - sum(Vt of Corresponding Neighboring Neurons) - Vt
2. Set Vt = 0 in indexes where Ut_1 < 0
3. Set Vt = 1 in indexes where Ut_1 > 3
4. Ut_1 = Ut
5. Check if Active Neurons (Neurons’s with 1s in Vt) graph is 2-degreed.
1. If so, Check if a Hamiltonian Tour exists. If yes, we have a solution.
6. If No solution has been found after an arbitrary number of attempts, try with new initial conditions.

## Code

import numpy as np
import itertools

knight_moves = np.array(
[[-2, -1], [-2, 1], [-1, -2], [-1, 2], [1, -2], [1, 2], [2, -1], [2, 1]])

def create_neurons(width, height):
neurons = set()
for i, j, (k, l) in itertools.product(range(width), range(height), knight_moves):
u, v = i+k, j+l
if 0 <= u < width and 0 <= v < height and (u, v, i, j) not in neurons:
return np.array(list(neurons))

def create_neighbour_bitfield(neurons):
neis = []
for i, j, u, v in neurons:
n1 = np.all(neurons[:, :2] == [i, j], axis=1)
n2 = np.all(neurons[:, 2:] == [i, j], axis=1)
n3 = np.all(neurons[:, :2] == [u, v], axis=1)
n4 = np.all(neurons[:, 2:] == [u, v], axis=1)
neigh = np.zeros(shape=(len(neurons)), dtype=np.int16)
neigh[np.argwhere(n1 | n2 | n3 | n4)] = 1
neis.append(neigh)
neis = np.array(neis)
np.fill_diagonal(neis, 0)
return neis.astype(np.bool)

def is_2_degree(Vt, N):
active_neurons = N[Vt.astype(np.bool)]
catted = np.concatenate(
(active_neurons[:, 2:], active_neurons[:, :2]), axis=0)
_, counts = np.unique(catted, return_counts=True, axis=0)
return len(counts[counts != 2]) == 0

def hamiltonian_cycle(N, Vt, width, height):
active_neurons = N[Vt.astype(np.bool)]
curr = [0, 0]
board = np.zeros((width, height), dtype=np.int16)
index = 0
while len(active_neurons) > 0:
board[curr[0], curr[1]] = index
index += 1
active_neighbours = active_neurons[
np.all(active_neurons[:, 2:] == curr, axis=1) |
np.all(active_neurons[:, :2] == curr, axis=1)
]
if len(active_neighbours) == 0:
return (False, [])
nex = active_neighbours[0]
active_neurons = active_neurons[np.logical_not(
np.all(active_neurons == nex, axis=1))]
curr = nex[2:] if np.all(nex[:2] == curr) else nex[:2]
return (True, board)

def knights_tour(width, height):
while True:
N = create_neurons(width, height)
Vt = np.random.randint(2, size=(len(N)), dtype=np.int16)
Ut = np.zeros(shape=(len(N)), dtype=np.int16)
G = create_neighbour_bitfield(N)

for _ in range(40):
Vt_tile = np.tile(Vt, (len(N), 1))
Ut_1 = Ut + 3 - np.sum(Vt_tile * G, axis=1) - Vt
if np.count_nonzero(Ut != Ut_1) == 0:
break
Vt[np.argwhere(Ut_1 < 0).ravel()] = 0
Vt[np.argwhere(Ut_1 > 3).ravel()] = 1
Ut = Ut_1

if is_2_degree(Vt, N):
tour_found, tour = hamiltonian_cycle(N, Vt, width, height)
if tour_found: