Basically, it performs sorting in increasing the cost of the path to a node. Also, always expands the least cost node. Although, it is identical to Breadth-First search if each transition has the same cost. It explores paths in the increasing order of cost.

The Uniform Cost Search (UCS) algorithm is a variant of Dijkstra’s algorithm used in the field of artificial intelligence for searching graphs or trees. It explores nodes in a graph or tree by gradually expanding outward from the starting node to the goal node while considering the cost associated with each path.

The key characteristic of UCS is that it always selects the node with the lowest cost so far to expand next. This makes UCS particularly useful in scenarios where the cost of reaching each node varies, and the objective is to find the lowest-cost path to the goal node.

Here’s a high-level overview of how UCS works:

Initialize an empty priority queue (often implemented using a min-heap) to store nodes to be expanded.
Add the starting node to the priority queue with a cost of zero.
While the priority queue is not empty: a. Remove the node with the lowest cost from the priority queue. b. If the removed node is the goal node, return the path to it. c. Otherwise, expand the node by generating its successors and adding them to the priority queue with their respective costs.
Repeat step 3 until the goal node is found or the priority queue is empty.

The Uniform Cost Search algorithm guarantees finding the optimal solution in terms of the total cost of the path from the starting node to the goal node. However, it can be inefficient if the cost of reaching nodes is high and there are many nodes to explore. Additionally, it requires keeping track of the explored nodes and their associated costs, which might consume a considerable amount of memory in large graphs.