Greedy Search - artificial intelligence:
If we have a heuristic function for states, defined as above, so we can simply measure each state with respect to this measure and order the agenda items in terms of the score of the state in the item. However, at each stage, the agent determines which state scores lowest and puts agenda items on the top of the agenda that contain operators acting on that state. In this way, the most promising nodes in a search space are expanded before the less capable ones. This is a nature of best first search is usually called specifically as a greedy search.
In some situations, a greedy search can lead to a solution very quickly. In fact, a greedy search can often go down blind alleys that look promising to start with, but we don't lead to a solution ultimately. Often the best states at the start of a search are in fact really quite poor in comparison to those further in the search space. One way is to counteract this blind-alley effect is to turn off the heuristic until a proportion of the search space has been covered, that the truly high scoring states can be identified. One more problem with a greedy search is, which the agent will have to keep a record of which states have been explored in order to avoid repetitions and ultimately end up in a cycle, so that's why a greedy search must keep all the agenda items it has undertaken in its memory. And also, this search strategy is not optimal, is sense cause the optimal solution may have nodes on the path that score badly for the heuristic function, and hence a non-optimal solution will be found before an optimal one.