Key characteristics of BFS
- First-in-First-Out (FIFO): The FIFO queue is typically preferred in BFS because the FIFO queue will be faster than a priority queue and often results in the correct order of nodes. When FIFO is applied to BFS, new nodes deeper in the search tree are added to the back of the queue. The old nodes which are swallower than the new nodes get expanded first.
- Early goal test: In traditional BFS implementations, the algorithm maintains a set of states reached during the search process. However, instead of storing all reached states, it selectively stores only the set of reached states that allow for an early goal test. This test involves checking whether the newly generated node meets the goal criteria as soon as it is generated.
- Cost-optimal: BFS always aims to find a solution with a minimum cost prioritizing the shortest path, when BFS generates nodes at a certain depth d, it has already explored and generated all the nodes at the previous depth d-1. Consequently, if a solution exists within a search space, BFS can discover it as soon as it reaches that depth level. Therefore, BFS is said to be a cost-optimal solution.
Note: Triangular marker indicates the next node to be expanded at each stage.
Breadth First Search (BFS) for Artificial Intelligence
In artificial intelligence, the Breadth-First Search (BFS) algorithm is an essential tool for exploring and navigating various problem spaces. By systematically traversing graph or tree structures, BFS solves tasks such as pathfinding, network routing, and puzzle solving. This article probes into the core concepts of BFS, its algorithms, and practical applications in AI.
Table of Content
- What is Breadth-First Search?
- Key characteristics of BFS
- Breadth First Search (BFS) Algorithms
- How Breadth-First Search can help in Robot Pathfinding
- Practical Implementations of BFS in Pathfinding of Robots
- Conclusion
- FAQs on Breadth First Search (BFS) for Artificial Intelligence