Artificial Intelligence: Path Finding Algorithms

Explores the depth-first search algorithm, showing how DFS traverses nodes by depth to build a path to a target and discusses trade-offs between speed and optimality.

Learn depth-first search (dfs) for traversing graphs, comparing recursive and iterative implementations, and using a stack to print all nodes or stop when a target node is found.

Implement a DFS (depth-first search) in Python to find a path between two nodes, using a stack and a discovered set, expanding neighbors and preparing for target-path extraction.

Explore python implementation – part 2 for path finding with depth-first and breadth-first search, returning the path, tracking costs, and validating node comparisons.

Explore the intuition and implementation of breadth-first search, contrast with dfs, and understand when bfs is optimal for constant-cost steps, including a python queue example.

Learn theory and implementation of the A* path finding algorithm. Use a priority queue to select nodes by estimated cost g(n)+h(n), with Manhattan or Euclidean heuristics, and compare to DFS/BFS.

Explore graph traversal algorithms on discrete and continuous maps using dfs and bfs, compare path costs, and apply a distance-based cost in A*, achieving better solutions.

We conclude the course by presenting three pathfinding algorithms with a consistent structure, linking intuition and pseudocode to Python or Java, and inviting feedback for future AI and robotics courses.