Dynamic Knowledge Scope Control for AI Characters
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Solution Overview
Problem
Conventional AI character models are limited by static knowledge databases, preventing them from dynamically adapting to changing circumstances, user interactions, or environmental variations, thus failing to provide a responsive and context-aware virtual character experience.
Innovation Solution
A system and method for dynamically controlling the knowledge scope of AI characters by associating a knowledge store with each AI character, determining context parameters, defining a scope of knowledge, constraining access to the knowledge store, and dynamically adjusting the scope based on changes in context parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a static knowledge database is used for AI characters, then the system complexity is reduced and ease of manufacture is improved, but the adaptability and context-awareness of the AI characters deteriorate
Solution Approach 1:
The patent implements dynamic control of knowledge scope by allowing the AI character's accessible knowledge to change over time based on context parameters. The system transitions from a static knowledge database to a dynamic knowledge scope that expands and contracts based on situational requirements, enabling the AI character to adapt to changing contexts while maintaining a manageable underlying knowledge base.
Solution Approach 2:
The patent changes the parameter of knowledge accessibility by introducing context parameters that dynamically adjust which portions of the knowledge database are available to the AI character. This allows the system to control the effective knowledge scope through parameter adjustment rather than requiring multiple static knowledge bases, balancing adaptability with system complexity.
2Adaptability or versatility
If a large comprehensive knowledge database is provided to AI characters, then the knowledge scope and versatility are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the necessary portions of the knowledge database that are relevant to the current context and makes them accessible to the AI character. Instead of providing the entire knowledge database simultaneously, the system selectively extracts and presents relevant knowledge subsets, reducing the effective complexity while maintaining comprehensive knowledge availability when needed.
Solution Approach 2:
The patent segments the large knowledge database into manageable portions that can be selectively accessed based on context parameters. By dividing the knowledge base into context-dependent segments and controlling access to specific segments rather than the entire database, the system reduces computational complexity while maintaining comprehensive knowledge coverage.
3Reliability
If the knowledge scope is dynamically adjusted based on context parameters, then the context-awareness and responsiveness are improved, but the computational overhead and processing time increase
Solution Approach 1:
The patent performs preliminary organization of the knowledge database and establishes context parameter frameworks in advance, allowing for faster dynamic adjustment during runtime. By preparing the knowledge structure and context parameters beforehand, the system reduces the computational overhead and processing time required for real-time knowledge scope adjustment while maintaining high context-awareness.
Data Source
AI summary
Systems and methods for dynamically controlling a knowledge scope of artificial intelligence (AI) characters are provided. An example method includes associating a knowledge store with an AI character generated by an AI character model, where the AI character is a virtual character in a virtual world; determining context parameters for the AI character; defining, based on the context parameters, a scope of knowledge available to the AI character from a knowledge store; constricting access of the AI character to the knowledge store based on the defined scope of knowledge; monitoring changes in the context parameters; and dynamically adjusting the scope of knowledge based on the changes in the context parameters.