Patent ID: 11928591
Assignee: HITACHI, LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. An information processing apparatus for controlling active sensing using a sensor, comprising:
a storage device that stores therein a learning model corresponding to each of a plurality of categories and obtained by learning, and the categories to each of which a previously recognized object to be sensed belongs;
a model processor configured to select one or a plurality of the learning models from among the learning models stored in the storage device;
an observation data latent variable analyzer configured to calculate a probability distribution of a latent variable given observation data obtained by sequentially compounding pieces of sensor data applied from the sensor, the probability distribution of the latent variable being output from each of the learning models selected by the model processor when the observation data is input to each of the learning models;
an observation data information amount analyzer configured to calculate a probability distribution of an information amount owned by the observation data at a next cycle on a basis of the probability distribution of the latent variable of the observation data calculated by the observation data latent variable analyzer and the observation data; and
a sensing action processor configured to select a setting of sensing at the next cycle on a basis of the probability distribution of the information amount owned by the observation data, the probability distribution of the information amount being calculated by the observation data information amount analyzer, wherein
the model processor
selects the learning model corresponding to the category estimated as the category to which a current object to be sensed belongs with a highest probability on a basis of the categories to each of which the previously recognized object to be sensed stored in the storage device belongs; and wherein

the model selection processor
selects the learning models corresponding to a predetermined number of top categories high in total number of objects to be sensed belonging to each of the categories, among the categories to each of which the previously recognized object to be sensed belongs.