Artificial intelligence for solving domain-specific problems

A method for providing explainable artificial intelligence, comprising:

• receiving, at a computing device, a domain-specific dataset corresponding to a domain-specific problem;
• identifying, based on balancing an estimated minimum of information required to solve the domain-specific problem and an estimated amount of computation time required to solve the domain-specific problem, an optimal level of description for a domain corresponding to the domain-specific problem;
• generating, based on the domain-specific dataset, a plurality of candidate features,
• wherein generating the plurality of candidate features comprises:
• transforming one or more portions of the domain-specific dataset to correspond to the optimal level of description;
• and
• generating, based on the transforming and using a combinatorial algorithm, a plurality of combinations of features corresponding to the optimal level of description and included in the domain-specific dataset;
• reducing, by performing one or more feature reduction operations, the plurality of candidate features into an optimized feature set, wherein the one or more feature reduction operations comprise one or more of:
• selecting, based on based on one or more predictive scores indicating a likelihood of a given candidate feature predicting a solution to the domain-specific problem, a subset of candidate features,
• eliminating one or more redundant features, or
• compressing the plurality of candidate features to combine features exceeding a threshold similarity score;
• training, based on the optimized feature set, a predictive model, wherein training the predictive model configures the predictive model to output solutions to domain-specific problems and representations of decisions made by the predictive model to output the solutions to domain-specific problems;
• outputting, using the predictive model, a solution for the domain-specific problem and a representation of decisions made by the predictive model to output the solution;
• and
• applying, based on the representation of decisions made by the predictive model to output the solution, one or more decisions made by the predictive model to at least one additional domain-specific problem.

A computing system comprising:

• one or more processors;
• and
• memory storing computer executable instructions that, when executed by the one or more processors, cause the computing system to:
• receive a domain-specific dataset corresponding to a domain-specific problem;
• identify, based on balancing an estimated minimum of information required to solve the domain-specific problem and an estimated amount of computation time required to solve the domain-specific problem, an optimal level of description for a domain corresponding to the domain-specific problem;
• generate, based on the domain-specific dataset, a plurality of candidate features,
• wherein generating the plurality of candidate features comprises:
• transforming one or more portions of the domain-specific dataset to correspond to the optimal level of description;
• and
• generating, based on the transforming and using a combinatorial algorithm, a plurality of combinations of features corresponding to the optimal level of description and included in the domain-specific dataset;
• reduce, by performing one or more feature reduction operations, the plurality of candidate features into an optimized feature set, wherein the one or more feature reduction operations comprise one or more of:
• selecting, based on based on one or more predictive scores indicating a likelihood of a given candidate feature predicting a solution to the domain-specific problem, a subset of candidate features,
• eliminating one or more redundant features, or
• compressing the plurality of candidate features to combine features exceeding a threshold similarity score;
• train, based on the optimized feature set, a predictive model, wherein training the predictive model configures the predictive model to output solutions to domain-specific problems and representations of decisions made by the predictive model to output the solutions to domain-specific problems;
• output, using the predictive model, a solution for the domain-specific problem and a representation of decisions made by the predictive model to output the solution;
• and
• apply, based on the representation of decisions made by the predictive model to output the solution, one or more decisions made by the predictive model to at least one additional domain-specific problem.