kotlin coding test course, maze exploration

1. Introduction

There are various ways to prepare for coding tests, but the ability to solve algorithm problems is particularly important. In this course, we will learn the basics of search algorithms through maze exploration problems and implement them using Kotlin. Maze exploration can be approached using various search algorithms such as DFS (Depth-First Search) and BFS (Breadth-First Search). This time, we will proceed with maze exploration using BFS.

2. Problem Definition

We will solve the problem of finding the shortest path from the starting point to the destination in the given maze. The maze is represented as a 2D array, where ‘0’ indicates a traversable space and ‘1’ indicates an impassable wall. We assume that the starting point is (0, 0) and the destination is (N-1, M-1).

Example:
0 0 1 0
1 0 1 0
0 0 0 0
0 1 1 0

In the maze above, we need to explore the shortest path from (0, 0) to (3, 3).

3. Approach to Problem Solving

To solve this problem, we will use the BFS (Breadth-First Search) algorithm. BFS is suitable for shortest path problems and explores neighboring nodes step by step to reach the goal. The process of exploring the maze using BFS is as follows:

1. Add the starting point to the queue and initialize the visit record.
2. Dequeue a node from the queue and explore its adjacent nodes.
3. If an adjacent node is the destination point, terminate the search.
4. After exploring all possibilities, determine if it is possible to reach the destination point.

4. Kotlin Implementation

Now, let’s implement the BFS algorithm using Kotlin.

fun bfs(maze: Array): Int {
    val n = maze.size
    val m = maze[0].size
    val directions = arrayOf(Pair(0, 1), Pair(1, 0), Pair(0, -1), Pair(-1, 0))
    val queue: Queue> = LinkedList()
    queue.add(Pair(0, 0))
    val visited = Array(n) { BooleanArray(m) }
    visited[0][0] = true
    var steps = 0

    while (queue.isNotEmpty()) {
        val size = queue.size
        for (i in 0 until size) {
            val (x, y) = queue.poll()
            // Check if reached the destination point
            if (x == n - 1 && y == m - 1) {
                return steps
            }
            // Explore adjacent nodes
            for (dir in directions) {
                val newX = x + dir.first
                val newY = y + dir.second
                if (newX in 0 until n && newY in 0 until m && maze[newX][newY] == 0 && !visited[newX][newY]) {
                    visited[newX][newY] = true
                    queue.add(Pair(newX, newY))
                }
            }
        }
        steps++
    }
    return -1 // If unable to reach the destination point
}

In the above code, we first define the size of the maze and the exploration directions, and initialize the BFS queue. As we proceed with the exploration, we check each node for adjacent nodes and add them to the queue. If we reach the destination point, we return the number of steps taken; otherwise, we return -1 to handle the case where reaching the destination is impossible.

5. Verification of Results

Now, let’s run the above code to explore the example maze. Below is the maze array and an example of function calls:

fun main() {
    val maze = arrayOf(
        intArrayOf(0, 0, 1, 0),
        intArrayOf(1, 0, 1, 0),
        intArrayOf(0, 0, 0, 0),
        intArrayOf(0, 1, 1, 0)
    )
    val result = bfs(maze)
    println("The length of the shortest path is: $result")
}

When this code is executed, the length of the shortest path will be printed. In the given example, the result is 7, which indicates the shortest path from the starting point to the destination point.

6. Conclusion

In this course, we learned to use the BFS algorithm to solve the maze exploration problem and how to implement it in Kotlin code. Since this is one of the commonly asked problem types in coding tests, it is important to practice similar problems thoroughly.

If future problems become more complex, it is also a good idea to consider other search algorithms such as the A* algorithm. Understanding and implementing algorithms will enhance your skills. Keep challenging yourself and continue to improve your abilities!