02-12-2021, 04:08 PM
(This post was last modified: 02-12-2021, 04:16 PM by dale walton.)
Thanks for the explanation. In the game, I noticed the AIs racking up better game-states (score spreads) and ignoring nearby wins, that could be obtained simply by passing a few turns and allowing the point spread to lessen, but not turnaround.
Position must include the fact of a previous pass though, if it alters the rules for what can be done. If the fact of the previous play being a pass is not part of the game-state, that would explain it. This tells me that the problem is that I implemented it as a condition and maybe there is a way to record it as part of a game state.
Here what essentially happens is that the state of the previous pass changes the meaning of the patterns of the pieces (something like who is to play changes their meaning) so the spacial patterns would need another set of probabilities assigned depending on whether the move is after a pass or not after a pass. - In fact this is not as symmetrical as who is to play where the probabilities can be assumed to be complementary. it would rather require a separate model for the two states.
--- or the pass state would need to be a significant part of most of the positional patterns the system recognises...
Does the AI take all the state variables as inputs, or just board position?
Position must include the fact of a previous pass though, if it alters the rules for what can be done. If the fact of the previous play being a pass is not part of the game-state, that would explain it. This tells me that the problem is that I implemented it as a condition and maybe there is a way to record it as part of a game state.
Here what essentially happens is that the state of the previous pass changes the meaning of the patterns of the pieces (something like who is to play changes their meaning) so the spacial patterns would need another set of probabilities assigned depending on whether the move is after a pass or not after a pass. - In fact this is not as symmetrical as who is to play where the probabilities can be assumed to be complementary. it would rather require a separate model for the two states.
--- or the pass state would need to be a significant part of most of the positional patterns the system recognises...
Does the AI take all the state variables as inputs, or just board position?