Weighted move rating for AI











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My AI (for the card game schnapsen) currently calculates every possible way the game could end and then evaluates the percentage of winning for every playable card.
The calculation is done recursively using a tree. If a game could move on in three different ways the percentage of winning on this node would be mean * (1 - (standardDeviation * f)) * 100 where f is between 0 and 2. When the game can't move on and the AI wins the percentage is 100, when lost 0. I'm including the standard deviation in this formula to prevent the AI from risking too much.



Is there a better formula or way of calculating the next move to maximize the chance of winning? Does including the standard deviation make sense?






algorithm artificial-intelligence







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    This is a pretty big question on a very big subject. i would suggest asking it at ai.stackexchange.com
    – Dinari
    Nov 11 at 12:19















up vote
1
down vote

favorite












My AI (for the card game schnapsen) currently calculates every possible way the game could end and then evaluates the percentage of winning for every playable card.
The calculation is done recursively using a tree. If a game could move on in three different ways the percentage of winning on this node would be mean * (1 - (standardDeviation * f)) * 100 where f is between 0 and 2. When the game can't move on and the AI wins the percentage is 100, when lost 0. I'm including the standard deviation in this formula to prevent the AI from risking too much.



Is there a better formula or way of calculating the next move to maximize the chance of winning? Does including the standard deviation make sense?






algorithm artificial-intelligence







share|improve this question


















  • 2




    This is a pretty big question on a very big subject. i would suggest asking it at ai.stackexchange.com
    – Dinari
    Nov 11 at 12:19













up vote
1
down vote

favorite









up vote
1
down vote

favorite











My AI (for the card game schnapsen) currently calculates every possible way the game could end and then evaluates the percentage of winning for every playable card.
The calculation is done recursively using a tree. If a game could move on in three different ways the percentage of winning on this node would be mean * (1 - (standardDeviation * f)) * 100 where f is between 0 and 2. When the game can't move on and the AI wins the percentage is 100, when lost 0. I'm including the standard deviation in this formula to prevent the AI from risking too much.



Is there a better formula or way of calculating the next move to maximize the chance of winning? Does including the standard deviation make sense?






algorithm artificial-intelligence







share|improve this question













My AI (for the card game schnapsen) currently calculates every possible way the game could end and then evaluates the percentage of winning for every playable card.
The calculation is done recursively using a tree. If a game could move on in three different ways the percentage of winning on this node would be mean * (1 - (standardDeviation * f)) * 100 where f is between 0 and 2. When the game can't move on and the AI wins the percentage is 100, when lost 0. I'm including the standard deviation in this formula to prevent the AI from risking too much.



Is there a better formula or way of calculating the next move to maximize the chance of winning? Does including the standard deviation make sense?






algorithm artificial-intelligence




algorithm artificial-intelligence






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share|improve this question











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asked Nov 11 at 12:12









GraxCode

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  • 2




    This is a pretty big question on a very big subject. i would suggest asking it at ai.stackexchange.com
    – Dinari
    Nov 11 at 12:19














  • 2




    This is a pretty big question on a very big subject. i would suggest asking it at ai.stackexchange.com
    – Dinari
    Nov 11 at 12:19








2




2




This is a pretty big question on a very big subject. i would suggest asking it at ai.stackexchange.com
– Dinari
Nov 11 at 12:19




This is a pretty big question on a very big subject. i would suggest asking it at ai.stackexchange.com
– Dinari
Nov 11 at 12:19

















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