# 7.13. Implementing Knight’s Tour¶

The search algorithm we will use to solve the knight’s tour problem is
called **depth-first search** (**DFS**). Whereas the
breadth-first search algorithm builds
a search tree one level at a time, a depth-first search creates a search
tree by exploring one branch of the tree as deeply as possible. In this
section we will look at two algorithms that implement a depth-first
search. The first algorithm we will look at specifically solves the knight’s
tour problem by explicitly forbidding a node to be visited more than
once. The second implementation is more general, but allows nodes to be
visited more than once as the tree is constructed. The second version is
used in subsequent sections to develop additional graph algorithms.

The depth-first exploration of the graph is exactly what we need in order to find a path through 64 vertices (one for each square on the chessboard) and 63 edges. We will see that when the depth-first search algorithm finds a dead end (a place in the graph where there are no more moves possible) it backs up the tree to the next deepest vertex that allows it to make a legal move.

The `knight_tour`

function shown in Listing 3
takes four parameters: `n`

, the current
depth in the search tree; `path`

, a list of vertices visited up to
this point; `u`

, the vertex in the graph we wish to explore; and
`limit`

, the number of nodes in the path. The `knight_tour`

function
is recursive. When the `knight_tour`

function is called, it first
checks the base case condition. If we have a path that contains 64
vertices, we return from `knight_tour`

with a status of `True`

,
indicating that we have found a successful tour. If the path is not long
enough, we continue to explore one level deeper by choosing a new vertex
to explore and calling `knight_tour`

recursively for that vertex.

**Listing 3**

```
from pythonds3.graphs import Graph
def knight_tour(n, path, u, limit):
u.color = "gray"
path.append(u)
if n < limit:
neighbors = sorted(list(u.get_neighbors()))
i = 0
done = False
while i < len(neighbors) and not done:
if neighbors[i].color == "white":
done = knight_tour(n + 1, path, neighbors[i], limit)
i = i + 1
if not done: # prepare to backtrack
path.pop()
u.color = "white"
else:
done = True
return done
```

DFS also uses colors to keep track of which vertices in the graph have
been visited. Unvisited vertices are colored white, and visited vertices
are colored gray. If all neighbors of a particular vertex have been
explored and we have not yet reached our goal length of 64 vertices, we
have reached a dead end and must backtrack.
Backtracking happens when we return from `knight_tour`

with a status of
`False`

. In the breadth-first search we used a queue to keep track of
which vertex to visit next. Since depth-first search is recursive, we
are implicitly using a stack to help us with our backtracking. When we
return from a call to `knight_tour`

with a status of `False`

, in line 11,
we remain inside the `while`

loop and look at the next
vertex in `neighbors`

.

Let’s look at a simple example of `knight_tour`

(Listing 3) in action. You
can refer to the figures below to follow the steps of the search. For
this example we will assume that the call to the `get_neighbors`

method on line 6 orders the nodes in
alphabetical order. We begin by calling `knight_tour(0, path, A, 6)`

.

`knight_tour`

starts with node A in Figure 3. The nodes adjacent to A are B and D.
Since B is before D alphabetically, DFS selects B to expand next as
shown in Figure 4. Exploring B happens when `knight_tour`

is
called recursively. B is adjacent to C and D, so `knight_tour`

elects
to explore C next. However, as you can see in Figure 5 node C is
a dead end with no adjacent white nodes. At this point we change the
color of node C back to white. The call to `knight_tour`

returns a
value of `False`

. The return from the recursive call effectively
backtracks the search to vertex B (see Figure 6). The next
vertex on the list to explore is vertex D, so `knight_tour`

makes a
recursive call moving to node D (see Figure 7). From vertex D on,
`knight_tour`

can continue to make recursive calls until we
get to node C again (see Figure 8, Figure 9, and Figure 10). However, this time when we get to node C the
test `n < limit`

fails so we know that we have exhausted all the
nodes in the graph. At this point we can return `True`

to indicate
that we have made a successful tour of the graph. When we return the
list, `path`

has the values `[A, B, D, E, F, C]`

, which is the order
we need to traverse the graph to visit each node exactly once.

Figure 11 shows you what a complete tour around an \(8 \times 8\) board looks like. There are many possible tours; some are symmetric. With some modification you can make circular tours that start and end at the same square.