There are different types of games. Such as 3X3 eight-tile, 4X4 fifteen-tilepuzzles are single-agent-path-finding challenges. As they are consisting of a matrix of tiles with a blank tile. Thus, to arrange the tiles by sliding a tile either vertically or horizontally into a blank space. Also, with the aim of accomplishing some objective.

In the context of artificial intelligence, a single agent pathfinding problem refers to the task of finding a path from a starting point to a goal location in a given environment while navigating through obstacles or barriers. This problem is encountered in various fields such as robotics, video games, logistics, and route planning.

Key components of a single agent pathfinding problem include:

Environment: The space in which the agent operates. This could be represented as a grid, a graph, or a continuous space.
Start and Goal States: The initial position of the agent (start state) and the destination it needs to reach (goal state).
Actions: The set of possible movements or transitions the agent can make from one state to another. These actions are typically subject to constraints imposed by the environment, such as barriers or obstacles.
Cost or Distance Metric: The measure of cost associated with moving from one state to another. This could be distance traveled, time taken, or any other relevant metric.

Solving a single agent pathfinding problem involves finding an optimal or satisfactory path from the start state to the goal state while adhering to the constraints of the environment and minimizing some cost function, such as distance traveled or time taken. Popular algorithms for solving such problems include A* search, Dijkstra’s algorithm, and various heuristic search algorithms.

In an interview setting, a comprehensive answer would not only define the problem but also discuss common algorithms used to solve it, their complexities, and perhaps real-world applications or challenges associated with single agent pathfinding.