Informed (Heuristic) Search Strategies: Greedy best-first search

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Informed (Heuristic) Search Strategies use domain-specific knowledge, also known as heuristics, to guide the search process towards the goal. These strategies can be more efficient than uninformed search strategies as they use additional information about the problem to make informed decisions about which paths to explore.

One of the informed search strategies is the Greedy Best-First Search algorithm. This algorithm uses a heuristic function that estimates the cost from the current node to the goal node. The algorithm selects the node that appears to be the closest to the goal, as measured by the heuristic function, as the next node to expand. It does not consider the cost of the path to reach that node.

Greedy Best-First Search algorithm is a greedy algorithm because it makes the best possible decision at each step without considering the future consequences. This means that it may not always find the optimal solution, but it can often find a good solution quickly.

The Greedy Best-First Search algorithm is commonly used in applications such as pathfinding, navigation, and robotics. It can be applied to a variety of search problems, including graph traversal and constraint satisfaction.

One limitation of the Greedy Best-First Search algorithm is that it can get stuck in local minima, where it finds a path that appears to be optimal but is not the best overall. Additionally, it may not consider the cost of reaching the selected node, which can lead to suboptimal solutions.

In summary, the Greedy Best-First Search algorithm is an informed search strategy that uses a heuristic function to estimate the cost from the current node to the goal node. It selects the node that appears to be the closest to the goal as the next node to expand. While it can be a fast and efficient algorithm, it may not always find the optimal solution and can get stuck in local minima.

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