A system and method for defining and calibrating the inputs to a sequential decision problem using historical data, where the user provides historical data and the system and method forms the historical data (along with other inputs) into at least one of the states, actions, rewards or transitions used in composing and solving the sequential decision problem.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-aided decision making system, comprising: (a) a user input device; (b) a user output device; and (c) a processor programmed to evaluate decision problems available to a user, the programmed processor; (A) facilitating input of a historical data set from the decision maker via the user input device; (B) the programmed processor defining a decision problem to be solved, the decision problem defined by parameters generated using statistical techniques on the historical data set, the parameters including; (i) an action set, the action set has elements representing actions available to the subject and the costs of performing the actions, (ii) at least one state dimension representing conditions relevant to the decision problem, (iii) a reward set representing rewards received by the user when transitioning between states for actions in the action set, (iv) a transition matrix containing the probability of moving between the states for each action in the action set, (v) a time index and a discount factor, the time index containing decision points available to the user where the user selects an action from the action set, and the discount factor representing the subject's preference for rewards relative to time, (C) the programmed processor combining the reward set with the action costs to form a reward matrix and the programmed processor combining the transition matrices with the action set to form a total transition matrix; (D) the programmed processor forming a functional equation from the state dimensions, the reward matrix, the total transition matrix, and all remaining parameters; (E) the programmed processor evaluating the functional equation, including error-checking and validating the parameters and performing a convergence check to ensure that the functional equation will be solvable, and the programmed processor solving the functional equation; (F) the programmed processor generating an optimal policy by using the solved functional equation to find, for every point in the decision index, the overall value-maximizing action, the set of decisions made at each point in the decision index forming the optimal policy; (G) the programmed processor outputting the optimal policy to the user through the user output device. 2. A computer-aided decision making system according to claim 1, wherein the programmed processor generates the set of state dimensions, the action costs, the time index and the discount factor using at least one of: K-means, K nearest neighbors, or hierarchical clustering, or Bayes or Naïve-Bayes classification 3. A computer-aided decision making system according to claim 1, wherein the programmed processor receives the historical data and at least one additional input of: an action, a state, a discount factor, a decision point, a reward for the reward set, an element of the transition matrix, or an action cost, the programmed processor generating the remaining parameters including the additional input in the statistical techniques. 4. A computer-aided decision making system according to claim 1, wherein the programmed processor receives the historical data and the set of state dimensions, the action set, the action cost set, the discount factor, the time index and the programmed processor uses the historical data to generate the reward set and the set of transition matrices. 5. A computer-aided decision making system according to claim 4, wherein the programmed processor uses the historical data to generate the parameters including modifying at least one of the set of state dimensions, the action set, the action cost set, the discount factor or the time index. 6. A computer-aided decision making system according to claim 4, wherein the programmed processor receives the historical data and at least one reward for the reward set or one element of a transition matrix and uses the historical data and the additional provided elements to generate the reward set and the set of transition matrices. 7. A computer-aided decision making system according to claim 1, wherein the programmed processor prompts the user to review and edit at least one of the parameters of the decision problem generated from the historical data before forming the functional equation, the programmed processor allowing the user to edit at least one of the historical data or the parameters before forming the functional equation. 8. A computer-aided decision making system according to claim 4, wherein the programmed processor prompts the user to review and edit at least one of the parameters of the decision problem generated from the historical data before forming the functional equation, the programmed processor allowing the user to edit at least one of the historical data or the parameters before forming the functional equation. 9. A computer-aided decision making system according to claim 1, wherein the programmed processor prompts the user to review and edit at least one of the parameters of the decision problem generated from the historical data after viewing the optimal policy and the programmed processor reforming and solving the edited decision problem for the user. 10. A computer-aided decision making system according to claim 4, wherein the programmed processor prompts the user to review and edit at least one of the parameters of the decision problem generated from the historical data after viewing the optimal policy and the programmed processor reforming and solving the edited decision problem for the user. 11. A computer implemented method for assisting a user in making a decision comprising: (a) providing a computer system having a user input device, a user output device and a processor programmed with instructions to evaluate decision problems available to the user, the instructions programming the processor and; (A) using the computer system to facilitate input of a historical data set from the decision maker via the user input device; (B) defining a decision problem to be solved, the decision problem defined by parameters generated using statistical techniques on the historical data set, the parameters including; (i) an action set, the action set has elements representing actions available to the subject and the costs of performing the actions, (ii) at least one state dimension representing conditions relevant to the decision problem, (iii) a reward set representing rewards received by the user when transitioning between states for each action in the action set, (iv) a transition matrix containing the probability of moving between the states for actions in the action set, (v) a time index and a discount factor, the time index containing decision points available to the user where the user selects an action from the action set, and the discount factor representing the subject's preference for rewards relative to time, (C) combining the reward set with the action costs to form a reward matrix and combining the transition matrices with the action set to form a total transition matrix; (D) forming a functional equation from the state dimensions, the reward matrix, the total transition matrix, and all remaining parameters; (E) evaluating the functional equation, including error-checking and validating the parameters and performing a convergence check to ensure that the functional equation will be solvable, and the programmed processor solving the functional equation; (F) generating an optimal policy by using the solved functional equation to find, for every point in the decision index, the overall value-maximizing action, the set of decisions made at each point in the decision index forming the optimal policy; (G) outputting the optimal policy to the user through the user output device. 12. A method as set forth in claim 11, wherein the step of generating the set of state dimensions, the action costs, the time index and the discount factor using at least one of: K-means, K nearest neighbors, or hierarchical clustering, or Bayes or Naïve-Bayes classification 13. A method as set forth in claim 11, wherein the step of receiving the historical data also includes at least one additional input of: an action, a state, a discount factor, a decision point, a reward for the reward set, an element of the transition matrix, or an action cost, and generating the remaining parameters while including the additional input in the statistical techniques. 14. A method as set forth in claim 11, wherein the step of receiving the historical data also includes the set of state dimensions, the action set, the action cost set, the discount factor, the time index and using the historical data to generate the reward set and the set of transition matrices. 15. A method as set forth in claim 14, wherein the step of using the historical data to generate the parameters further includes modifying at least one of the set of state dimensions, the action set, the action cost set, the discount factor or the time index. 16. A method as set forth in claim 14, wherein the step of receiving the historical data further includes at least one reward for the reward set or one element of a transition matrix and using the historical data and the additional provided elements to generate the reward set and the set of transition matrices. 17. A method as set forth in claim 11, wherein an additional step prompts the user to review and edit at least one of the parameters of the decision problem generated from the historical data before forming the functional equation and further includes the step of allowing the user to edit at least one of the historical data or the parameters before forming the functional equation. 18. A method as set forth in claim 14 wherein an additional step prompts the user to review and edit at least one of the parameters of the decision problem generated from the historical data before forming the functional equation and further includes the step of allowing the user to edit at least one of the historical data or the parameters before forming the functional equation. 19. A method as set forth in claim 11 wherein an additional step prompts the user to review and edit at least one of the parameters of the decision problem generated from the historical data after viewing the optimal policy and reforming and solving the edited decision problem for the user. 20. A method as set forth in claim 14 wherein the programmed processor prompts the user to review and edit at least one of the parameters of the decision problem generated from the historical data after viewing the optimal policy and reforming and solving the edited decision problem for the user.