I tried making it better by applying probabilistic statistics to the game tree, quite like antropic reasoning. It then became quite bad at playing.
Ordinary minimax with A-B did very well.
Game algorithms that ignore density of states in the game tree, and only focus on minimaxing, do much better. This is a close analogy to the experience trees of Eliezer, and therefore a hint that antropic reasoning here has some kind of error.
That’s because those games are nonrandom, and your opponent can be expected to single out the best move.
Algorithms for games like backgammon and poker that have a random element, do pay attention to density of states.
(Oddly enough, so nowadays do the best known algorithms for Go, which surprised almost everyone in the field when this discovery was made. Intuitively, this can be seen as being because the game tree of Go is too large and complex for exhaustive search to work.)
I have an Othello/Reversi playing program.
I tried making it better by applying probabilistic statistics to the game tree, quite like antropic reasoning. It then became quite bad at playing.
Ordinary minimax with A-B did very well.
Game algorithms that ignore density of states in the game tree, and only focus on minimaxing, do much better. This is a close analogy to the experience trees of Eliezer, and therefore a hint that antropic reasoning here has some kind of error.
Kim0
That’s because those games are nonrandom, and your opponent can be expected to single out the best move.
Algorithms for games like backgammon and poker that have a random element, do pay attention to density of states.
(Oddly enough, so nowadays do the best known algorithms for Go, which surprised almost everyone in the field when this discovery was made. Intuitively, this can be seen as being because the game tree of Go is too large and complex for exhaustive search to work.)