Multiple projects regarding to Artificial Intelligence course

Designing and implementation of:

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BFS, DFS, IDS, UCS (Uninformed Search Strategies)

Breadth-first Search(BFS)

Trees and graphs can be traversed using breadth-first search strategies. It is called breadth-first search because it searches a tree or graph breadthwise.
Before moving to nodes of the next level, the BFS algorithm searches from the root node of the tree and expands all successor nodes at the current level.
An example of a general-graph search algorithm is breadth-first search.
FIFO queue data structure is used for breadth-first search.

Depth-first Search(DFS)

Trees or graphs can be traversed using depth-first search as a recursive algorithm.
In depth-first search, each path is followed to its greatest depth node before moving on to the next path.
In order to implement DFS, a stack data structure is used.
As with BFS, the DFS algorithm follows a similar process

bfs

Iterative Deepening Search(IDS)

By combining depth-first and breadth-first search (for nodes closer to the root), IDS provides efficient space management and fast searching at the same time.
IDS calls DFS for different depths based on an initial value. Each call of DFS is limited to a given depth. We do DFS in a BFS manner.

ids

Uniform-cost Search(UCS)

Uniform-cost search is a searching algorithm used for traversing a weighted tree or graph. When each edge has a different cost, this algorithm is used.
A uniform-cost search seeks the path with the lowest cumulative cost to the goal node. Using uniform-cost search, nodes are expanded according to their path costs from the root node.
It can be used to solve any graph/tree where the optimal cost is in demand.
A uniform-cost search algorithm is implemented by the priority queue.
It gives maximum priority to the lowest cumulative cost.
Uniform cost search is equivalent to BFS algorithm if the path cost of all edges is the same.