Providing a predetermined return-to-player for a skill-based wagering machine

1. A computing device that provides a skill-based game of a wagering machine with a predetermined return-to-player, the computing device comprising:

a memory storing instructions; and

a processor that executes the instructions, wherein execution of the instructions, results in the processor:

defining classes of game states, wherein each class of game states includes game states sharing a common set of game state characteristics that lead to a randomly selected game state;

determining, for each class of game states, an expected minimum payout for the respective class of game states;

constructing, for each initial game state of a plurality of initial game states, a decision tree that includes a root node, intermediary nodes, leaf nodes, and collective leaf nodes, wherein the root node represents a respective initial game state, each intermediary node represents an intermediary game state achievable from the initial game state, each leaf node represents a game outcome achievable from the respective initial game state, and each collective leaf node represents a class of game states and its expected minimum payout;

determining, based on the decision tree for each initial game state, an expected minimum payout for the respective initial game state; and

generating, for the wagering machine, a table that weights each initial game state of the plurality of initial game states based on its respective expected minimum payout to achieve a desired minimum return-to-player.

2. The computing device of claim 1 , wherein execution of the instructions further results in the processor generating the table such that the weights associate each initial game state with a probability of the wagering machine selecting the initial game state from the plurality of initial game states.

3. The computing device of claim 1 , wherein execution of the instructions further results in the processor:

determining, for each class of game states, an expected maximum payout for the respective class of game states;

constructing the decision tree for each initial game state such that the collective leaf nodes further represents the expected maximum payout for the represented class of game states;

determining, based on the decision tree for each initial game state, an expected maximum payout for the respective initial game state; and

weighting each initial game state in the table based further upon its respective expected maximum payout to achieve a desired maximum return-to-player.

4. The computing device of claim 1 , wherein execution of the instructions further results in the processor assigning a zero weight to one or more initial game states in the table to prevent the wagering machine from random selecting the one or more initial game states as an initial game state for the skill-based game.

5. The computing device of claim 1 , wherein execution of the instructions further results in the processor associating each class of game states with a quantity of remaining moves in the skill-based game.

6. The computing device of claim 1 , wherein execution of the instructions further results in the processor associating each class of game states with a particular game state characteristic.

7. The computing device of claim 6 , wherein the particular game state characteristic includes a number of hits remaining to activate a symbol.

8. The computing device of claim 1 , wherein execution of the instructions further results in the processor constructing the decision tree, for one of the initial game states, with at least one collective leaf node that represents hundreds of game states.

9. The computing device of claim 1 , wherein execution of the instructions further results in the processor constructing the decision tree, for one of the initial game states, with at least collective leaf node that represents thousands of game states.

10. A method of one or more computing devices for providing a predetermined return-to-player for a skill-based game of wagering machine, the method comprising:

defining, with the one or more computing devices, classes of game states, wherein each class of game states includes game states sharing a common set of game state characteristics that lead to a randomly selected game state;

determining, with the one or more computing devices for each class of game states, an expected minimum payout for the respective class of games states;

constructing, with the one or more computing devices for each initial game state of a plurality of initial game states, a decision tree that includes a root node, intermediary nodes, leaf nodes, and collective leaf nodes, wherein the root node represents a respective initial game state, each intermediary node represents an intermediary game state achievable from the initial game state, each leaf node represents a game outcome achievable from the respective initial game state, and each collective leaf node represents a class of game states and its expected minimum payout;

determining, with the one or more computing devices based on the decision tree for each initial game state, an expected minimum payout for the respective initial game state; and

generating, with the one or more computing devices for the wagering machine, a table that weights each initial game state of the plurality of initial games states based on its respective expected minimum payout to achieve a desired minimum return-to-player.

11. The method of claim 10 , further comprising:

determining, with the one or more computing devices for each class of game states, an expected maximum payout for the respective class of game states;

constructing, with the one or more computing devices, the decision tree for each initial game state such that each collective leaf node further represents the expected maximum payout for the represented class of game states;

determining, with the one or more computing devices based on the decision tree for each initial game state, an expected maximum payout for the respective initial game state; and

weighting, with the one or more computing devices, each initial game state in the table based further upon its respective expected maximum payout to achieve a desired maximum return-to-player.

12. The method of claim 10 , further assigning, with the one or more computing devices, a zero weight to one or more initial game states in the table to prevent the wagering machine from randomly selecting the one or more initial game states as an initial game state for the skill-based game.

13. The method of claim 10 , further comprising associating, with the one or more computing devices, each class of game states with a quantity of remaining moves in the skill-based game.

14. The method of claim 10 , further comprising further associating, with the one or more computing devices, each class of game states with a particular game state characteristic.

15. The method of claim 14 , wherein the particular game state characteristic includes a number of hits remaining to activate a symbol.

16. The method of claim 10 , further comprising constructing the decision tree, with the one or more computing devices for one of the initial game states, with at least one collective leaf node that represents hundreds of game states.

17. The method of claim 10 , further comprising constructing the decision tree, with the one or more computing devices, for one of the initial game states, with at least one collective leaf node that represents thousands of game states.

18. A non-transitory computer readable medium that provides a skill-based game of a wagering machine with a predetermined return-to-player, the non-transitory computer readable medium comprising instructions that, in response to being executed, result in a computing device:

defining classes of game states, wherein each class of game states includes game states sharing a common set of game state characteristics that lead to a randomly selected game state;

determining, for each class of game states, an expected minimum payout for the respective class of game states;

constructing, for each initial game state of a plurality of initial game states, a decision tree that includes a root node, intermediary nodes, leaf nodes, and collective leaf nodes, wherein the root node represents a respective initial game state, each intermediary node represents an intermediary game state achievable from the initial game state, each leaf node represents a game outcome achievable from the respective initial game state, and each collective leaf node represents a class of game states and its expected minimum payout;

determining, based on the decision tree for each initial game state, an expected minimum payout for the respective initial game state; and

generating, for the wagering machine, a table that weights each initial game state of the plurality of initial game states based on its respective expected minimum payout to achieve a desired minimum return-to-player.

19. The non-transitory computer readable medium of claim 18 , wherein execution of the instructions further results in the computing device:

determining, for each class of game states, an expected maximum payout for the respective class of game states;

constructing the decision tree for each initial game state such that each collective leaf node further represents the expected maximum payout for the represented class of game states;

determining, based on the decision tree for each initial game state, an expected maximum payout for the respective initial game state; and

weighting each initial game state in the table based further upon its respective expected maximum payout to achieve a desired maximum return-to-player.

20. The non-transitory computer readable medium of claim 18 , wherein execution of the instructions further results in the computing device associating each class of game states with a quantity of remaining moves in the skill-based game.