Patent application EP21176371A (EP), filed 2021-05-27
Publication number: EP3926550A1
Title: PROCESS AND SYSTEM INCLUDING EXPLAINABLE PRESCRIPTIONS THROUGH SURROGATE-ASSISTED EVOLUTION
Abstract: An explainable surrogate-assisted evolutionary optimization method, E-ESP, discovers rule-based decision strategies for which actions to take to achieve certain outcomes when historical training data is limited or unavailable. The resulting rules are human readable and thus facilitate explainability and trustworthiness unlike the black box solutions resulting from neural network solutions.
IPC codes: G06N  20/    00            A I
G06N   3/    12            A I
G06N   5/    04            A I
Claims: number: 1
text: A computer-implemented process for developing an optimized prescriptor model for determining optimal decision policy outcomes for a predetermined domain comprising:
number: 2
text: The computer-implemented process of claim 1, wherein the predictor surrogate model is trained on historical training data including context information (C), actions (A) performed in an included context, and outcome (O) in data sets (C, A, O).
number: 3
text: The computer-implemented process of claim 1, wherein the predictor surrogate model is a machine learning model trained with supervised methods.
number: 4
text: The computer-implemented process of claim 3, wherein the predictor surrogate model is selected from the group consisting of: a trained model, a labeled dataset and a simulator.
number: 5
text: The computer-implemented process of claim 1, wherein the rule-based prescriptor model has the general shape:  coefficient 1 ∗ input i lag 1 p 1 < | ≤ | > | ≥ coefficient 2 ∗ input j lag 2 p 2 | value ,
number: 6
text: The computer-implemented process of claim 5, wherein the optimized rule-based prescriptor model is selected based on a fitness evaluation.
number: 7
text: A computer-implemented process for finding a decision policy that optimizes a set of outcomes to solve a known policy problem, comprising:
number: 8
text: The computer-implemented process of claim 7, wherein the predictor model is a machine learning model trained with supervised methods.
number: 9
text: The computer-implemented process of claim 8, wherein the predictor model is selected from the group consisting of a trained model, a labeled dataset and a simulator.
number: 10
text: The computer-implemented process of claim 7, wherein the rule-based prescriptor model has the general shape:  coefficient 1 ∗ input i lag 1 p 1 < | ≤ | > | ≥ coefficient 2 ∗ input j lag 2 p 2 | value
number: 11
text: The computer-implemented process of claim 10, wherein the elite evolved rule-based prescriptor model is selected based on a fitness evaluation.
number: 12
text: A computer-readable medium storing instructions that, when executed by a computer, perform a process for developing an optimized prescriptor model for determining optimal decision policy outcomes for a predetermined domain comprising:
number: 13
text: The computer readable medium of claim 12, wherein the predictor surrogate model is trained on historical training data including context information (C), actions (A) performed in an included context, and outcome (O) in data sets (C, A, O).
number: 14
text: The computer readable medium of claim 12, wherein the predictor surrogate model is a machine learning model trained with supervised methods.
number: 15
text: The computer readable medium of claim 14, wherein the predictor surrogate model is selected from the group consisting of: a trained model, a labeled dataset and a simulator.
number: 16
text: The computer readable medium of claim 12, wherein the rule-based prescriptor model has the general shape:  coefficient 1 ∗ input i lag 1 p 1 < | ≤ | > | ≥ coefficient 2 ∗ input j lag 2 p 2 | value ,
number: 17
text: The computer readable medium of claim 16, wherein the optimized rule-based prescriptor model is selected based on a fitness evaluation.
Office action: search_report, dated 2021-12-22
Rejection reasons: novelty_epc_54
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severity: novelty_destroying
Final disposition: pending (2021-12-22)