Deep Learning based Quasi-consciousness Training for Robot Intelligent Model

TL;DR

This paper proposes a deep learning framework for developing quasi-consciousness in robots, enabling them to learn, reason, and generalize complex tasks through environmental modeling and extended training.

Contribution

It introduces a novel deep learning-based approach for quasi-consciousness training in robots, combining environmental factor matrices and prolonged anthropomorphic training.

Findings

Robots can fuse known concepts to generalize to new situations.

Environmental factor matrices enhance learning and reasoning.

Extended training develops primary-like consciousness in robots.

Abstract

This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. First, by constructing a network of environmental factor matrix to stimulate the learning process of the robot intelligent model, the model parameters must be subjected to coarse & fine tuning to optimize the loss function for minimizing the loss score, meanwhile robot intelligent model can fuse all previously known concepts together to represent things never experienced before, which need robot intelligent model can be generalized extensively. Secondly, in order to progressively develop a robot intelligent model with primary consciousness, every robot must be subjected to at least 1~3 years of special school for training anthropomorphic behaviour patterns to understand and process complex environmental information and make rational decisions. This work explores and delivers the potential application of deep learning-based quasi-consciousness training in the field of robot intelligent model.