Patent · US Active

Learning agent categories using agent trajectory clustering

US11485014B1 · kind B1 · utility

0Cited by
0References
18Claims
0Family size

Assignee

Inventor

Key dates

Filing dateNov 5, 2021
Grant dateNov 1, 2022
Priority date
Expiry dateNov 5, 2041

Classification

  • Technology area (CPC G)Physics
  • CPC primaryG05B2219/40323
  • WIPO fieldHandling
  • WIPO sectorMechanical engineering

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for selecting actions for an agent in an environment. In one aspect, a system comprises receiving an agent trajectory that characterizes interaction of an agent with an environment to perform one or more initial tasks in the environment; processing the agent trajectory to generate a classification output that comprises a respective classification score for each agent category in a set of possible agent categories, wherein each possible agent category is associated with a respective task selection policy; classifying the agent as being included in a corresponding agent category based on the classification scores; selecting tasks to be performed by the agent in the environment based on the task selection policy of the corresponding agent category; and transmitting, to the agent, data defining the selected tasks to be performed by the agent in the environment.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.