Well, for what I know, modern chess engines are relatevly small AI models that usually work by taking on input the current state of the board and then predicting the next best move. Like Stockfish. Also, there is a game called Supreme Commander 2, where it is confirmed of usage small neural models to run NPC. And, as a person that somewhat included in game development, I can say that indie game engine libgdx provides an included AI module that can be fine tuned to a needed level for running NPC decisions. And it can be scaled in any way you want.
As I understand, chess AIs are more like brute force models that take the current board and generate a tree with all possible moves from that position, then iterating on those new positions up to a certain depth (which is what the depth of the engine refers to). And while I think some might use other algorithms to “score” each position and try to keep the search to the interesting branches, that could introduce bias that would make it miss some moves that look bad but actually set up a better position, though ultimately, they do need some way to compare between different ending positions if the depth doesn’t bring them to checkmate in all paths.
So it chooses the most intelligent move it can find, but does it by essentially playing out every possible game, kinda like Dr Strange in Infinity War, except chess has a more finite set of states to search through.
Maybe. I haven’t studied modern chess engines so deeply. All I know that you either can use the brute force method that will calculate in recursion each possible move or train an AI model on existing brute force engines and it will simply guess the best possible move without actually recalculating each possible. Both scenarios work with each one having its own benefits and downsides.
But all of this is said according to my knowledge which can be incomplete, so recommend to recheck this info.
Well, for what I know, modern chess engines are relatevly small AI models that usually work by taking on input the current state of the board and then predicting the next best move. Like Stockfish. Also, there is a game called Supreme Commander 2, where it is confirmed of usage small neural models to run NPC. And, as a person that somewhat included in game development, I can say that indie game engine libgdx provides an included AI module that can be fine tuned to a needed level for running NPC decisions. And it can be scaled in any way you want.
As I understand, chess AIs are more like brute force models that take the current board and generate a tree with all possible moves from that position, then iterating on those new positions up to a certain depth (which is what the depth of the engine refers to). And while I think some might use other algorithms to “score” each position and try to keep the search to the interesting branches, that could introduce bias that would make it miss some moves that look bad but actually set up a better position, though ultimately, they do need some way to compare between different ending positions if the depth doesn’t bring them to checkmate in all paths.
So it chooses the most intelligent move it can find, but does it by essentially playing out every possible game, kinda like Dr Strange in Infinity War, except chess has a more finite set of states to search through.
Maybe. I haven’t studied modern chess engines so deeply. All I know that you either can use the brute force method that will calculate in recursion each possible move or train an AI model on existing brute force engines and it will simply guess the best possible move without actually recalculating each possible. Both scenarios work with each one having its own benefits and downsides.
But all of this is said according to my knowledge which can be incomplete, so recommend to recheck this info.