Autonomous Apparatus Emotion Prediction for Cooperative Control
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Solution Overview
Problem
Existing autonomous control techniques cannot implement cooperative control considering the emotions of individuals or other apparatuses, as they operate with independent action selection criteria.
Innovation Solution
An autonomous apparatus equipped with a processor, storage device, and interface, utilizing an internal model to predict environmental states and social emotions, decides actions based on observed data, self and social emotions, and future predictions to enable cooperative autonomous control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If independent action selection criteria are used for each apparatus, then each apparatus can autonomously decide its own actions, but cooperative autonomous control considering emotions of other individuals or apparatuses cannot be implemented
Solution Approach 1:
The patent introduces an internal model as an intermediary that enables each autonomous apparatus to predict and understand the mental states and emotions of other apparatuses. This internal model acts as a mediator that translates independent autonomous decisions into cooperative behavior by allowing each apparatus to anticipate others' actions and emotions, thereby resolving the contradiction between autonomous operation and cooperative control capability
Solution Approach 2:
The patent segments the autonomous control system into multiple independent apparatuses, each equipped with its own internal model and emotion prediction capability. This segmentation allows each apparatus to maintain its autonomous decision-making while simultaneously considering the emotional states of others, enabling cooperative control without sacrificing individual autonomy
Data Source
AI summary
A cooperative autonomous control is implemented in consideration of emotions of a person or another apparatus. An apparatus holds an internal model for predicting a future state of an environment, acquires observation data indicating a state of an environment observed by the apparatus, generates first state information using the observation data, generates second state information of an intelligent agent present around the apparatus based on the first state information, decides a self emotion of the apparatus based on the first state information, decides a self emotion of the intelligent agent based on the second state information, decides a social emotion based on self emotions of the apparatus and the intelligent agent, generates future first state information and future second state information using the internal model, and decides an action based on the future first state information, the future second state information, and the social emotion.