An Artificial Neural Network Interface (ANNI) is disclosed along with use cases for the same. The ANNI utilizes one or more decision trees and/or probabilistic/combinatoric analysis to determine optimal responses to current conditions. The ANNI is also enabled to learn new conditions that are accepted as normal and, in response thereto, update the decision tree(s).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: mining data related to conditions and variables of one or more events; based on the mined data, creating a decision tree that includes options for responding to each of the one or more events and probabilities of success for each of the options; and using an artificial intelligence agent to traverse the decision tree and, based on current conditions, determine, from the decision tree, a computer-selected optimal option for responding to the current conditions. 2. The method of claim 1, wherein the one or more events correspond to at least one of military events, health-related events, and network events. 3. The method of claim 1, further comprising: providing the information related to the one or more events to a genetic algorithm; processing the information related to the one or more events with the genetic algorithm; and determining, based on the processing of the one or more events with the genetic algorithm, whether to at least one of create and modify a rule set; and storing the rule set in a database. 4. The method of claim 3, wherein processing the information related to the one or more events with the genetic algorithm comprises: searching for anomalous behavior F*(x) that maps x to y, such that over a joint distribution of all (y, x) values, an expected value of a specified loss function is minimized. 5. The method of claim 4, wherein the specific loss function comprises: arg minF(x) E y,x Ψ(y, F(x)). 6. The method of claim 5, wherein boosting approximates F*(x) by an additive expansion of the form: F(x)=Σm=0Mβmh(x; am), wherein the functions h(x; a) correspond to base learner functions that are set by functions of x with parameters a={a1, a2, . . . , am}, and wherein expansion coefficients {βm}0M and the parameters {αm}0M are made fit to the training data in a forward stage-wise manner. 7. The method of claim 1, wherein the artificial intelligence agent is both language and data agnostic and learns at the byte level. 8. A non-transitory computer-readable medium comprising processor-executable instructions that, when executed by a processor, perform a method, the method comprising: mining data related to conditions and variables of one or more events; based on the mined data, creating a decision tree that includes options for responding to each of the one or more events and probabilities of success for each of the options; and using an artificial intelligence agent to traverse the decision tree and, based on current conditions, determine, from the decision tree, a computer-selected optimal option for responding to the current conditions. 9. The computer-readable medium of claim 8, wherein the one or more events correspond to at least one of military events, health-related events, and network events. 10. The computer-readable medium of claim 8, wherein the method further comprises: providing the information related to the one or more events to a genetic algorithm; processing the information related to the one or more events with the genetic algorithm; and determining, based on the processing of the one or more events with the genetic algorithm, whether to at least one of create and modify a rule set; and storing the rule set in a database. 11. The computer-readable medium of claim 10, wherein processing the information related to the one or more events with the genetic algorithm comprises: searching for anomalous behavior F*(x) that maps x to y, such that over a joint distribution of all (y, x) values, an expected value of a specified loss function is minimized. 12. The computer-readable medium of claim 11, wherein the specific loss function comprises: arg minF(x) E y,x Ψ(y, F(x)). 13. The computer-readable medium of claim 12, wherein boosting approximates F*(x) by an additive expansion of the form: F(x)=Σm=0Mβmh(x; am), wherein the functions h(x; a) correspond to base learner functions that are set by functions of x with parameters a={a1, a2, . . . , am}, and wherein expansion coefficients {βm}0M and the parameters {αm}0M are made fit to the training data in a forward stage-wise manner. 14. The computer-readable medium of claim 8, wherein the artificial intelligence agent is both language and data agnostic and learns at the byte level. 15. A computing device, comprising: computer memory having instructions stored thereon, the instructions including an artificial neural network interface that is configured, when executed, to mine data related to conditions and variables of one or more events, based on the mined data, create a decision tree that includes options for responding to each of the one or more events and probabilities of success for each of the options, and then traverse the decision tree to automatically select an optimal option for responding to the current conditions; and a processor configured to read the instructions stored in the memory and execute the instructions including the artificial neural network interface. 16. The computing device of claim 15, wherein the one or more events correspond to at least one of military events, health-related events, and network events. 17. The computing device of claim 15, wherein the artificial neural network interface is further configured, when executed by the processor, to process the information related to the one or more events with the genetic algorithm. 18. The computing device of claim 17, wherein the genetic algorithm searches for anomalous behavior F*(x) that maps x to y, such that over a joint distribution of all (y, x) values, an expected value of a specified loss function is minimized. 19. The computing device of claim 18, wherein the specific loss function comprises: arg minF(x) E y,x Ψ(y, F(x)), wherein boosting approximates F*(x) by an additive expansion of the form: F(x)=Σm=0Mβmh(x; am), wherein the functions h(x; a) correspond to base learner functions that are set by functions of x with parameters a={a1, a2, . . . , am}, and wherein expansion coefficients {βm}0M and the parameters {αm}0M are made fit to the training data in a forward stage-wise manner. 20. The computing device of claim 15, wherein the artificial neural network interface is both language and data agnostic and learns at the byte level.