3

Intuitively I kind of doubt expecting a search depth of 10 in half a second is reasonable, especially for the initial game state where there's a rather large branching factor and no immediately-winning moves that help to prune some branches quickly. I've never implemented any Alpha-Beta agents for Gomoku specifically, but I can provide some numbers for our ...


3

Both algorithms should give the same answer. However, their main difference is that alpha-beta does not explore all paths, like minimax does, but prunes those that are guaranteed not to be an optimal state for the current player, that is max or min. So, alpha-beta is a better implementation of minimax. Here are the time complexities of both algorithms ...


2

Some basic advantages of MCTS over Minimax (and its many extensions, like Alpha-Beta pruning and all the other extensions over that) are: MCTS does not need a heuristic evaluation function for states. It can make meaningful evaluations just from random playouts that reach terminal game states where you can use the loss/draw/win outcome. So if you're faced ...


1

You can't prune the nodes that are cross out if we search from left-to-right in the tree using alpha-beta pruning. To do this analysis we can pretend the right branch of the tree doesn't exist. (Branch C from the root.) In the left branch (A) of the root Helen will get 2 or more. In the middle branch (B) from the root after going down the left, Stavros ...


1

I think this issue stems from the fact you aren't taking position into account. I would think this because as the game progresses, the number of moves that will result in a piece being taken becomes less and less, especially when there's only a few pieces left and quite a bit of "chasing" must occur before a piece is taken, likely more chasing then a depth ...


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