ANALYSIS DEVICE, MACHINE LEARNING DEVICE, ANALYSIS SYSTEM, ANALYSIS METHOD, AND RECORDING MEDIUM

1 . An analysis device comprising:

at least one memory configured to store instructions; and

at least one processor configured to execute the instructions to:

apply, for each of a plurality of candidates for an updated parameter value set according to an update target parameter value, the update target parameter value and the candidate to a plurality of machine learning results to acquire, for each machine learning result, information indicating a degree of difference of an evaluation target value in a case of the candidate with respect to an evaluation target value in a case of the update target parameter value;

calculate, for each candidate and for each machine learning result, an evaluation target value in the case of the candidate based on the degree of difference of the evaluation target values and the evaluation target value in the case of the update target parameter value; and

calculate a selection index value for each candidate using a variation in the evaluation target values for each machine learning result;

compare the selection index value of each of the plurality of candidates;

select a candidate from the plurality of candidates based on a result of the comparison; and

update the update target parameter value and the evaluation target value in the case of the update target parameter value to the selected candidate and the evaluation target value in the case of the selected candidate, respectively.

2 . The analysis device according to claim 1 , wherein calculating the selection index value comprises setting the selection index value to a higher value as the variation is larger and selecting the candidate comprises selecting a candidate having a highest selection index value from the plurality of candidates.

3 . The analysis device according to claim 1 , wherein calculating the selection index value comprises calculating a selection index value for each candidate by using the variation for each machine learning result and an average value of the evaluation target value of each of the plurality of candidates.

4 . The analysis device according to claim 1 , wherein calculating the selection index value comprises performing look-ahead of update of the parameter value, and setting a selection index value to a higher value as a candidate has a smaller number of look-ahead parameter values, and selecting the candidate comprises selecting a candidate having a highest selection index value from the plurality of candidates.

5 . The analysis device according to claim 1 , wherein the at least one processor is configured to execute the instructions to acquire, for each of the plurality of candidates, a value normalized by dividing a difference of the evaluation target value in the case of the candidate with respect to the evaluation target value in the case of the update target parameter value by the evaluation target value in the case of the update target parameter value, as the information indicating the degree of difference of the evaluation target values.

6 . A machine learning device comprising:

at least one memory configured to store instructions; and

at least one processor configured to execute the instructions to:

acquire a plurality of sets of an update target parameter value and an updated parameter value;

calculate, for each of the plurality of sets, an evaluation target value in a case of the update target parameter value and an evaluation target value in a case of the updated parameter value by simulation;

calculate, for each of the plurality of sets, a degree of difference of the evaluation target value in the case of the updated parameter value with respect to the evaluation target value in the case of the update target parameter value; and

acquire a plurality of machine learning results of a relationship between: the update target parameter value and the updated parameter value; and the degree of difference of the evaluation target values, by using the update target parameter value, the updated parameter value and the degree of difference between the evaluation target value of the plurality of sets.

7 . An analysis system comprising:

a machine learning device; and

the analysis device according to claim 1 ,

wherein the machine learning device comprises:

at least one memory configured to store instructions; and

at least one processor configured to execute the instructions to:

acquire a plurality of sets of an update target parameter value and an updated parameter value;

calculate, for each of the plurality of sets, an evaluation target value in a case of the update target parameter value and an evaluation target value in a case of the updated parameter value by simulation;

calculate, for each of the plurality of sets, a degree of difference of the evaluation target value in the case of the updated parameter value with respect to the evaluation target value in the case of the update target parameter value; and

acquire, as the plurality of machine learning results, a plurality of machine learning results of a relationship between: the update target parameter value and the updated parameter value; and the degree of difference of the evaluation target values, by using the update target parameter value, the updated parameter value and the degree of difference between the evaluation target value of the plurality of sets.

8 . An analysis method executed by a computer, the method comprising:

applying, for each of a plurality of candidates for an updated parameter value set according to an update target parameter value, the update target parameter value and the candidate to a plurality of machine learning results to acquire, for each machine learning result, information indicating a degree of difference of an evaluation target value in a case of the candidate with respect to an evaluation target value in a case of the update target parameter value;

calculating, for each candidate and for each machine learning result, an evaluation target value in the case of the candidate based on the degree of difference of the evaluation target values and the evaluation target value in the case of the update target parameter value;

calculating a selection index value for each candidate using a variation in the evaluation target values for each machine learning result;

comparing the selection index value of each of the plurality of candidates;

selecting a candidate from the plurality of candidates based on a result of the comparison; and

updating the update target parameter value and the evaluation target value in the case of the update target parameter value to the selected candidate and the evaluation target value in the case of the selected candidate, respectively.

9 . (canceled)