Virtual Companion Emotion Modeling via Concept Maps
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Solution Overview
Problem
Existing AI agents lack believability for human observers due to the absence of a comprehensive psychological theory and their perception of the environment in machine language rather than human language, limiting their ability to assist humans effectively in tasks like product selection.
Innovation Solution
A software agent utilizing a concept map for semantic knowledge representation to model user experiences and emotions, calculating a stimulation measure that guides its behavior and recommendations, mimicking human-like perception and emotional responses to enhance user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing AI agents use machine language level (feature-based knowledge representation) to perceive the environment, then their computational efficiency is improved, but their believability to human observers deteriorates
Solution Approach 1:
The patent introduces concept maps as an intermediary layer between machine language processing and human language interaction. The concept map represents knowledge in a semantically meaningful structure that bridges the gap between computational efficiency and human believability, allowing the agent to process information efficiently while presenting human-understandable explanations
Solution Approach 2:
The patent replaces traditional feature-based knowledge representation with concept map-based semantic representation. This substitution enables the agent to maintain computational efficiency while achieving human-like perception and emotional responses, thereby improving believability without sacrificing productivity
2Device complexity
If existing AI agents lack comprehensive psychological theory background, then their development complexity is reduced, but their ability to model human emotions deteriorates
Solution Approach 1:
The patent incorporates psychological theory background and concept map construction in advance during the agent's learning process. By pre-establishing the concept map structure and psychological foundations before actual interaction, the system achieves sophisticated emotion modeling without excessive development complexity during deployment
Solution Approach 2:
The patent makes the concept map dynamic and adaptable, allowing it to evolve as the agent learns from interactions. This dynamic structure enables the agent to develop emotional modeling capabilities progressively, balancing development complexity with emerging reliability in emotion representation
Data Source
AI summary
The invention proposes that a software agent for assisting a user in a virtual environment defined by a first concept map, maintains a record of its experiences in the form of a second concept map. The concept maps are compared to obtain a measure of stimulation. The measure of stimulation is used to derive a comparison value indicative an emotional state of the user, and the comparison value is used in a reward function to guide the behavior of the software agent.