How can you implement pathfinding algorithms in game AI programming?

1 What is pathfinding?

Pathfinding is the process of finding the optimal or shortest path between two points in a graph. A graph is a data structure that consists of nodes and edges, where nodes represent locations and edges represent connections or distances between them. For example, a game map can be represented as a graph, where each tile or cell is a node and each adjacent tile or cell is an edge.

2 How to use A* algorithm?

A* is a popular and efficient pathfinding algorithm for game AI, combining the features of Dijkstra and breadth-first search algorithms. It uses a cost function to measure the distance traveled from the start node to the current node, and a heuristic function to estimate the remaining distance to the goal node. The algorithm selects the node with the lowest sum of these two functions and expands it until it reaches the goal or runs out of nodes. To implement A* in game AI programming, you need to define a data structure that stores information for each node (coordinates, parent node, cost function value, and heuristic function value), as well as a priority queue that sorts the nodes by their sum of cost and heuristic values. The steps are then as follows: initialize the priority queue with the start node and mark it as visited; while the priority queue is not empty and the goal node is not visited, dequeue the node with the lowest priority and mark it as current; if it is the goal node, exit the loop and return the path by tracing back its parent nodes; otherwise, for each neighbor node of the current node that is not visited or blocked, calculate its cost and heuristic values, enqueue it with these values and mark it as visited.

3 How to use Dijkstra algorithm?

Dijkstra algorithm is another pathfinding algorithm that uses a cost function to find the shortest path between two nodes in a graph. It differs from A* in that it does not utilize a heuristic function, instead exploring all potential routes until the optimal one is found. To implement Dijkstra in game AI programming, you need to define a data structure that stores the coordinates, parent node, and cost function value for each node, as well as a priority queue that sorts the nodes by their cost values. Then, you can perform the following steps: initialize the priority queue with the start node and mark it as visited; while the priority queue is not empty and the goal node is not visited, dequeue the node with the lowest priority and mark it as current; if the current node is the goal node, exit the loop and trace back its parent nodes to return the path; otherwise, for each neighbor node of the current node that is not visited and not blocked, calculate its cost function value by adding the current node's cost and edge weight between them, enqueue it with its parent and cost values, and mark it as visited.

4 How to use breadth-first search algorithm?

Breadth-first search algorithm is a pathfinding algorithm that explores the graph level by level, beginning from the start node and progressing to its neighbors. It does not utilize any cost or heuristic function, but it guarantees to find the shortest path if it exists. To implement breadth-first search in game AI programming, you need to define a data structure that stores information for each node such as its coordinates and parent node, as well as a queue that stores the nodes to be visited. Then, you can initialize the queue with the start node and mark it as visited. While the queue is not empty and the goal node is not visited, dequeue the node from the front of the queue and mark it as current; if it is the goal node, return the path by tracing back the parent nodes. Otherwise, for each neighbor node of the current node that is not visited or blocked, enqueue it with its parent and mark it as visited.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?