AI With Python – Gaming

Games are played with a strategy. Every player or team would make a strategy before starting the game and they have to change or build a new strategy according to the current situation(s) in the game.

Search Algorithms

You will have to consider computer games also with the same strategy as above. Note that Search Algorithms are the ones that figure out the strategy in computer games.

How it works

The goal of search algorithms is to find the optimal set of moves so that they can reach the final destination and win. These algorithms use the winning set of conditions, different for every game, to find the best moves.

Visualize a computer game as the tree. We know that a tree has nodes. Starting from the root, we can come to the final winning node, but with optimal moves. That is the work of search algorithms. Every node in such a tree represents a future state. The search algorithms search through this tree to make decisions at each step or node of the game.

Combinational Search

The major disadvantage of using search algorithms is that they are exhaustive in nature, which is why they explore the entire search space to find the solution that leads to wastage of resources. It would be more cumbersome if these algorithms need to search the whole search space for finding the final solution.

To eliminate such kind of problem, we can use combinational search which uses the heuristic to explore the search space and reduces its size by eliminating the possible wrong moves. Hence, such algorithms can save resources. Some of the algorithms that use a heuristic to search the space and save the resources are discussed here.

Read the full article here.

For more Articles visit here: insideAIML