公开(公告)号：US20240144303A1

公开(公告)日：2024-05-02

申请号：US18544856

申请日：2023-12-19

Applicant: NEC Corporation

Inventor： Shinji ITO

IPC: G06Q30/0201

CPC classification number: G06Q30/0201

Abstract: An optimization apparatus includes: a selection unit that selects, as a correction value, an element having a magnitude equal to or smaller than a predetermined value from among convex hulls of a policy set; an acquisition unit that acquires a result of execution of a second policy executed in a second round, the second round being a round a predetermined round before a first round for executing a first policy that is determined from among the policy set; a calculation unit that calculates an estimated value of a loss vector in the execution of the policy based on the result of the execution and the correction value selected in the second round; an update unit that updates a first probability distribution based on the estimated value; and a determination unit that determines a policy for a next round based on the updated first probability distribution.

公开(公告)号：US20210390574A1

公开(公告)日：2021-12-16

申请号：US17258590

申请日：2018-07-12

Applicant: NEC Corporation

Inventor： Kei TAKEMURA ,  Shinji ITO

IPC: G06Q30/02

Abstract: Provided is an information processing system including: a condition acquisition unit that acquires constraint information on an action and candidate information for each of a plurality of candidates targeted for the action; a reward function estimation unit that estimates a reward function used for calculating a reward in accordance with the action for each of the plurality of candidates based on the constraint information and the candidate information; and an action determination unit that determines a content of the action based on the reward function for each of the plurality of candidates.

公开(公告)号：US20210182702A1

公开(公告)日：2021-06-17

申请号：US16761071

申请日：2018-08-17

Applicant: NEC CORPORATION

Inventor： Shinji ITO ,  Ryohei FUJIMAKI

IPC: G06N5/04 ,  G06N20/00 ,  G06Q30/02

Abstract: A learning unit 81 generates a plurality of sample groups from samples to be used for learning, and generates a plurality of prediction models while inhibiting overlapping of a sample group to be used for learning among the generated sample groups. An optimization unit 82 generates an objective function based on an explained variable predicted by the prediction model and based on a constraint condition for optimization, and optimizes a generated objective function. An evaluation unit 83 evaluates an optimization result by using a sample group that has not been used in learning of a prediction model used for generating an objective function targeted for the optimization.

公开(公告)号：US20180336476A1

公开(公告)日：2018-11-22

申请号：US15778690

申请日：2016-08-29

Applicant: NEC Corporation

Inventor： Ryohei FUJIMAKI ,  Shinji ITO

IPC: G06N5/02 ,  G06N99/00

CPC classification number: G06Q10/04

Abstract: Provided is an information processing system which perform suitable optimization even if there are input data not observed in mathematical optimization. A learning unit 71 learns a predictive model on the basis of an explained variable and explanatory variables, the predictive model representing a relationship between explained variable and explanatory variables and being expressed by a function of the explanatory variables. A visualization unit 72 visualizes the predictive model. When receiving the operation from the user, an optimization unit 73 calculates an objective variable optimizing an objective function under constraints, the objective function using, as an argument, a predictive model visualized by the visualization unit 72.

公开(公告)号：US20250103670A1

公开(公告)日：2025-03-27

申请号：US18289582

申请日：2023-06-21

Applicant: NEC Corporation

Inventor： Tatsuya MATSUOKA ,  Shinji ITO

IPC: G06F17/11

Abstract: An information processing device 1X mainly includes an output means 15X. The output means outputs, as a predetermined number of solutions, candidates in which a minimum value of a distance between the solutions satisfies a predetermined condition among candidates used to select the predetermined number of solutions, when outputting the predetermined number of solutions, from a plurality of solutions in an optimization problem.

公开(公告)号：US20240095610A1

公开(公告)日：2024-03-21

申请号：US17768082

申请日：2019-10-24

Applicant: NEC Corporation

Inventor： Shinji ITO

IPC: G06Q10/04

CPC classification number: G06Q10/04

Abstract: A purpose of the present invention is to achieve highly accurate optimization for a submodular function when a policy is optimized without preparing data for machine learning. An optimization apparatus (100) according to the present invention includes a determination unit (110) that determines one or more execution policies from a predetermined policy in an objective function having diminishing marginal utility and convexity, a reward acquisition unit (120) that acquires reward, which is an execution result in the objective function for the determined execution policy, a calculation unit (130) that calculates an update rate of the policy based on the reward, and an update unit (140) that updates the policy based on the update rate.

公开(公告)号：US20210142414A1

公开(公告)日：2021-05-13

申请号：US17054262

申请日：2018-05-14

Applicant: NEC Corporation

Inventor： Shinji ITO

IPC: G06Q40/06 ,  G06N5/02

Abstract: A measure determination system 80 determines a measure when an observed effect of the measure changes with time. An optimization unit 81 optimizes, based on the observed effect, an implementation ratio of the measure in such a manner as to maximize the effect to be multiplicatively accumulated. A reliability calculation unit 82 calculates, based on the optimized implementation ratio and the observed effect, reliability of each measure. A measure determination unit 83 determines a measure with higher reliability. An observation unit 84 observes an effect of the determined measure. The optimization unit 81 updates a past implementation ratio based on the observed effect, and the reliability calculation unit 82 updates the reliability of each measure based on the updated implementation ratio.

公开(公告)号：US20210027109A1

公开(公告)日：2021-01-28

申请号：US17043329

申请日：2018-10-29

Applicant: NEC Corporation

Inventor： Shinji ITO ,  Ryohei FUJIMAKI

IPC: G06K9/62 ,  G06N20/00

Abstract: A learning unit 81 generates a plurality of sample groups from samples used for learning, each of the sample groups containing at least one of samples not contained in the other sample groups, and generates a plurality of prediction models using each of the generated sample groups. An optimization unit 82 generates objective functions, represented by the sum of a plurality of functions, on the basis of explained variables predicted by the prediction models and constraints for optimization, and optimizes the generated objective functions. An evaluation unit 83 evaluates a result of the optimization for each of the objective functions.

公开(公告)号：US20250029155A1

公开(公告)日：2025-01-23

申请号：US18713772

申请日：2021-12-02

Applicant: NEC Corporation

Inventor： Kazuma SHIMIZU ,  Shinji ITO ,  Shinji NAKADAI

IPC: G06Q30/0283 ,  G06Q10/02 ,  G06Q50/40

Abstract: Based on a relation model representing a relationship between first data and second data, second data in an evaluation period is calculated from first data in the evaluation period. An evaluation value pertaining to the evaluation period is calculated by using an evaluation model and the calculated second data in the evaluation period, the evaluation model including the second data as a parameter. First data in the evaluation period in a case in which the calculated evaluation value increases are determined.

公开(公告)号：US20240394560A1

公开(公告)日：2024-11-28

申请号：US18695904

申请日：2021-10-04

Applicant: NEC CORPORATION

Inventor： Shinji ITO

IPC: G06N5/01

Abstract: An information processing apparatus includes: a selection section for selecting a subset Xt⊆[n] of a set [n] in a certain round t∈[T] with reference to an observation value of an objective function in a round t−1; and an output section for outputting information indicating the subset Xt⊆[n] which has been selected by the selection section, the selection section selecting the subset Xt⊆[n] so that an asymptotic behavior of an expected value of a regret Σt∈[T]ft(Xt)−Σt∈[T]ft(X*), which is expressed using an observation value ft(Xt) of an objective function in each round t∈[T] and a comparative solution X*, is bounded from above by an upper limit value A(Δ,n,C) which depends at least on a gap indicator Δ in a stochastic model and on a corruption indicator C indicating an adversarial corruption of the stochastic model.