Meta knowledge in AI refers to knowledge about knowledge itself.
In the realm of artificial intelligence, meta knowledge is a key concept. It is officially defined as a term used in the field of artificial intelligence to describe the knowledge of pre-defined knowledge.
Think of it not just as knowing facts (like "Paris is the capital of France"), but knowing how you know those facts, how to acquire new facts, how to organize facts, or how to use facts effectively to solve problems.
Examples of Meta Knowledge in AI
The reference provides several core examples of meta knowledge in action within AI systems:
- Planning: Knowing how to devise a sequence of actions to achieve a goal. This isn't just performing actions, but knowing the steps involved in planning itself (e.g., understanding preconditions, effects, and optimal sequencing).
- Tagging: Knowledge about how to label or categorize information. This involves understanding the relationships between tags, the criteria for applying them, and how tagging helps in retrieval or organization.
- Learning: Knowing how to learn or improve performance over time. This includes understanding different learning algorithms, when to apply them, how to evaluate performance, and how to refine the learning process itself.
These examples highlight the AI system's understanding of its own processes and information structure.
Evolution and Specification
The reference notes that the model embodying meta knowledge evolves over time and employs a different specification. This suggests that the understanding of how an AI system knows and processes information is not static. It can adapt and change as the system learns and interacts, potentially adopting new ways (specifications) of representing or utilizing its knowledge of knowledge.
In essence, meta knowledge empowers AI systems to be more flexible, robust, and capable of complex reasoning by giving them insight into their own operational knowledge base and processes.
