A * algorithm (go on)

A *AlgorithmArtificial intelligence is a typical heuristic search algorithm. To clarify the * algorithm, let me first talk about it.

What is a heuristic search algorithm?
Before you start, you need to search for the status space. Status space search. If it is based on a professional point, the problem solving process is represented by the process of searching for this path from the initial state to the target State. In layman's terms, when solving a problem, the process of finding a problem can start from the problem to the result of the problem (as though not popular ). There are many branches in the process of solving the problem, mainly because of the uncertainty of the conditions for solving the problem in the process of solving the problem, which is caused by incompleteness. This constitutes a picture of the many paths for solving the problem, we say this figure is the state space. The problem is actually solved by finding a path in this figure from the beginning to the result. The search process is the state space search.
Frequently Used status space searches have priority in depth and breadth. Breadth First is to find the target layer by layer until the target is found. Depth first searches for one branch in a certain order, and then finds another branch until the target is found. These two algorithms are described in the data structure book. You can refer to these books for more detailed explanations.
One major drawback of the breadth and depth preference search mentioned above is that they all work in a given State Space. This is a suitable algorithm when the state space is not large, but it is not advisable when the state space is large and unpredictable. His efficiency is too low to be completed. Heuristic Search is required here.
Heuristic Search is to evaluate the position of each search in the state space to obtain the best position, and then search until the target. In this way, a large number of fearless search paths can be omitted, and efficiency is mentioned. In heuristic search, location estimation is very important. Different estimates can have different effects. Let's first look at how the valuation is expressed.
In the inspiration, the valuation is represented by the valuation function, for example:

F (n) = g (n) + H (N)

Where F (n) is the estimated function of node N, the actual cost of g (n) from the initial node to the N node in the real state space, H (N) it is the estimated cost of the optimal path from N to the target node. Here, H (n) mainly reflects the inspiration information of search, because g (n) is known. For details, g (n) indicates the priority of search breadth. However, when H (n)> G (N), g (n) can be omitted to improve efficiency. This is deep, and it will not be affected if you don't understand it! Let's continue to look at what a * algorithm is.