What is the difference between uniform cost search (UCS) and A* algorithm?
The Correct Answer and Explanation is:
Correct Answer:
The difference between Uniform Cost Search (UCS) and A* lies in how they prioritize nodes during the search process.
- UCS selects the node with the lowest path cost (g(n)) from the start node.
- A* selects the node with the lowest estimated total cost (f(n) = g(n) + h(n)), where h(n) is a heuristic estimate of the remaining cost to the goal.
Explanation:
Uniform Cost Search (UCS) and A* algorithm are both search strategies used in Artificial Intelligence to find the shortest path in a graph or problem space. While they are similar in some ways, they differ in their approach to node selection during the search process.
UCS is a special case of Dijkstra’s algorithm. It expands the node with the smallest cumulative cost from the starting point. This cost is known as g(n), which represents the actual cost to reach a node n from the start. UCS does not use any heuristic information, meaning it has no knowledge about the goal’s location beyond what it has explored. This makes UCS complete and optimal for graphs with non-negative edge costs, but it can be slow since it explores many unnecessary nodes, especially when the goal is far or the search space is large.
On the other hand, A* algorithm enhances UCS by incorporating a heuristic function h(n). This function estimates the cost to reach the goal from the current node. A* uses a combination of actual cost and estimated cost: f(n) = g(n) + h(n). This allows A* to focus its search toward the goal, potentially skipping over irrelevant paths. When the heuristic is admissible (never overestimates the cost) and consistent (satisfies the triangle inequality), A* is both complete and optimal.
In summary, while UCS is uninformed and uses only actual cost, A* is informed and uses both actual and estimated costs to make smarter decisions during the search. A* is generally more efficient than UCS when a good heuristic is available.
