公开(公告)号：US20200242512A1

公开(公告)日：2020-07-30

申请号：US16737949

申请日：2020-01-09

Applicant: FUJITSU LIMITED

Inventor： Chaoliang ZHONG ,  Jun SUN

IPC: G06N20/00 ,  G06N5/04 ,  B25J9/16

Abstract: An information processing method comprises: generating an action sequence pair of a first action sequence of a first agent and a second action sequence of a second agent, the first and second action sequences performing an identical task; training a mapping model using the generated action sequence pair such that it is capable of generating an action sequence of the second agent according to an action sequence of the first agent; training a judgment model using the first action sequence of the first agent such that it is capable of judging whether a current action of an action sequence of the first agent is a last action of the action sequence; and constructing a mapping library using the trained mapping model and the trained judgment model, wherein the mapping library comprises a mapping from observation information of the second agent to an action sequence of the second agent.

公开(公告)号：US20210073591A1

公开(公告)日：2021-03-11

申请号：US17012357

申请日：2020-09-04

Applicant: FUJITSU LIMITED

Inventor： Chaoliang ZHONG ,  Ziqiang SHI ,  Wensheng XIA ,  Jun SUN

IPC: G06K9/62 ,  G06N3/08

Abstract: A robustness estimation method, a data processing method, and an information processing apparatus are provided. The method for estimating robustness a classification model obtained in advance through training based on a training data set, includes: for each training sample in the training data set, determining a target sample in a target data set that has a sample similarity with a respective training sample that is within a predetermined threshold range, and calculating a classification similarity between a classification result of the classification model with respect to the respective training sample and a classification result of the classification model with respect to the determined respective target sample; and determining, based on classification similarities between classification results of respective training samples in the training data set and classification results of corresponding target samples in the target data set, classification robustness of the classification model with respect to the target data set.