SKATEBIRD ALPHA BETA GAMER FULL
SKATEBIRD ALPHA BETA GAMER PLUS

Quote by John McCarthy from Human-Level AI is harder than it seemed in 1955 on the Dartmouth workshop:Ĭhess programs catch some of the human chess playing abilities but rely on the limited effective branching of the chess move tree. Knuth and Moore‘s famous Function F2, aka AlphaBeta Knuth already introduced an iterative solution, see Iterative Search Knuth's node types Samuel Fuller, John Gaschnig, James Gillogly, Analysis 1973.

Daniel Edwards and Timothy Hart, Description in 1961.Allen Newell and Herbert Simon Approximation in 1958.John McCarthy proposed the idea of Alpha-Beta after the representation of the Chess Program by Alex Bernstein at the Dartmouth Workshop in 1956.The minimal number of leaves is shown in following table which also demonstrates the odd-even effect:Īlpha-Beta was invented independently by several researchers and pioneers from the 50s, and further research until the 80s, most notable by

SKATEBIRD ALPHA BETA GAMER PLUS

Considering always the best move first, it is b ^ ceil(n/2) plus b ^ floor(n/2) minus one. As pointed out by Levin in 1961, assuming constantly b moves for each node visited and search depth n, the maximal number of leaves in alpha-beta is equivalent to minimax, b ^ n. Conversely, if we always searched worse moves before the better moves, we wouldn't be able to cut any part of the tree at all! For this reason, good move ordering is very important, and is the focus of a lot of the effort of writing a good chess program. Of course, we don't always know what the best move is, or we wouldn't have to search in the first place. If you always search the best possible move first, you eliminate the most of the nodes.

How many nodes you can actually cut, however, depends on how well ordered your game tree is. If a standard minimax search tree has x nodes, an alpha beta tree in a well-written program can have a node count close to the square-root of x. The savings of alpha beta can be considerable. One player's lower bound is the other player's upper bound. But we also have to maintain an upper bound because if a move at depth 3 or higher leads to a continuation that is too good, the other player won't allow it, because there was a better move higher up on the game tree that he could have played to avoid this situation. Now we have to maintain both a lower bound and an upper bound (called Alpha and Beta.) We maintain a lower bound because if a move is too bad we don't consider it. The situation becomes even more complicated, however, when we go to a search depth of 3 or greater, because now both players can make choices affecting the game tree. We know that we can achieve at least that, so anything that is clearly worse can be ignored.

SKATEBIRD ALPHA BETA GAMER FULL

The full analysis of Possible Move #1 gave us a lower bound. Maybe another possible response wins a Queen, but it doesn't matter because we know that we can achieve at least an even game by playing Possible Move #1. We really don't care exactly how much worse Possible Move #2 is. Then, we move on and consider another of White's possible moves (Possible Move #2.) When we consider the first possible counter-move by black, we discover that playing this results in black winning a Rook! In this situation, we can safely ignore all of Black's other possible responses to Possible Move #2 because we already know that Possible Move #1 is better.

After this analysis, we determine that the result of making Possible Move #1 is an even position. We consider this move and every possible response to this move by black.

Say it is White's turn to move, and we are searching to a depth of 2 (that is, we are consider all of White's moves, and all of Black's responses to each of those moves.) First we pick one of White's possible moves - let's call this Possible Move #1.

Skatebird alpha beta gamer