Autonomous Learning Method and System for Human-Machine Collaborative Agents

Abstract

The invention belongs to the technical field of artificial intelligence. The invention discloses an autonomous learning method and system of an agent for man-machine cooperative work. The method comprises the steps of obtaining a cooperation data set, training a cooperation agent and a simulation agent according to the cooperation data set; and assessing whether the cooperation agent and the simulation agent meet assessment requirements or not according to the obtained assessment data generated by cooperation of the trained cooperation agent and the simulation agent in the environment, judgingwhether the trained simulation agent needs new imitation learning or not if the assessment requirements are met, and ending autonomous learning of the trained cooperation agent if the assessment requirements are not met. The system comprises a cooperation agent, a simulation agent and a server. Through the scheme, the dynamic change of the environment can be adapted, the same performance effect can be obtained in the similar environment, the demonstration behaviors of different teaches can be simulated, so that the trained intelligent agent can adapt to the dynamic change of the teaches, andthe teaches with different operation levels can also achieve the same cooperation effect.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to an autonomous learning method and system for an intelligent body oriented to man-machine collaborative work. Background technique [0002] In the human-machine collaboration mode, the agent (the entity can be a robot) cooperates with humans to complete corresponding tasks, so the agent needs to have the ability to cooperate with humans. [0003] In the prior art, the agent can have this ability through an expert system. The expert system uses the knowledge and problem-solving methods of human experts to deal with problems in this field through a large number of expert-level knowledge and experience in the field. The expert system will constantly update and summarize the knowledge and experience of experts, and form a knowledge base in combination with rule programming, but it cannot adapt to the dynamic changes in the complex environment. In addition, ...

AI Technical Summary

Problems solved by technology

The expert system will constantly update and summarize the knowledge and experience of experts, and form a knowledge base in combination with rule programming, but it cannot adapt to the dynamic changes in the complex environment. In addition, it learns and trains the model through the guidance of experts. The demonstration cost of experts is high, and only It can represent the level of experts and cannot adapt to the dynamic changes of participants

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