What is Meta Knowledge in Artificial Intelligence?

Meta-knowledge is a type of data that describes knowledge in the domain of artificial intelligence. It is not domain-specific but rather seeks to characterise the organisation of data in knowledge systems. The primary use of meta-knowledge is to comprehend and enhance the character of user interface components, as well as to maintain knowledge bases that are utilised in conjunction with inference engines. With the rising complexity of the challenges we face, it is safe to predict that substantial usage of meta-knowledge will be witnessed in the next years. Many aspects of a system are critical for autonomous operations to perform well, such as control systems, support subsystems, and architecture.

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Implementing metaknowledge aids in the inter-domain interaction among modules that collaborate to solve challenges. Boost your Skills by learning: Digital Marketing
 
Table of Content:
1) What is Artificial Intelligence Knowledge Representation?
2) Types of Knowledge Representation
3) The relationship between knowledge and intelligence
4) Knowledge Representation Properties

What is Artificial Intelligence Knowledge Representation?
Information representation is the process by which artificial intelligence expresses information not through stored data in the system but through past experiences and knowledge in order to operate like an intelligent human. Do you comprehend what makes humans different from machines? Intelligence? No, since that is exactly what AI does - it mimics human intellect. One feature that distinguishes humans from robots is our conscience (the sum of all our knowledge thus far), or the ability to think and reason. Humans rely on this capacity to complete every activity in our lives. For example, we are aware that touching a hot skillet might cause our hands to burn even before we contact it. This is the sophisticated manner of working of the human mind, and if we want to transmit this complex information to a machine, we need to provide AI with more advanced information, which resulted in the notion of Knowledge Representation in AI.
 
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Types of Knowledge Representation:
Declarative Understanding:
It is the knowledge segment that seems to be static in nature and preserves factual information in memory. The realm of such knowledge determines the link between occurrences or entities.

• Procedural Knowledge:

This information is more specific than declarative knowledge and is also known as imperative knowledge. It has the capability of revealing the accomplishment of a given job. It is frequently employed by current mobile robots, which may be trained to conquer a building or manoeuvre within a room.

• Meta-awareness:

In the field of artificial intelligence, meta-knowledge is a phrase used to express the knowledge of pre-defined information. Some instances of metaknowledge are as follows: Learning, labelling, and Planning, are some instances of meta knowledge. This model changes and applies a varied specification throughout time. Precision, relevance, evaluation, predictability, comprehensiveness, disambiguation, rationalisation, life expectancy, purpose, source, and dependability are just a few types of meta-knowledge that a knowledge engineer could employ.

• Heuristic Understanding: This is also known as superficial knowledge, and it works on the rule of thumb. It is extremely useful in the reasoning process since it answers issues based on past problem records or problems prepared by experts. It provides insights based on previous challenges it has handled.

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The relationship between knowledge and intelligence:
Knowledge of real-world environments is essential for intelligence and for producing artificial intelligence. Knowledge is essential for AI bots to demonstrate intelligent behaviour. An agent can only act properly on some information if he has knowledge or expertise with that input. Assume you meet someone who is speaking in a language you don't understand, and you have no idea what to do. The same is true of the agents' intelligent behaviour. As seen in the picture below, there is one decision-maker who acts by sensing the environment and applying knowledge. However, if the knowledge component is missing, intelligent behaviour cannot be displayed.
 
Knowledge Representation Properties:

• Inferential sufficiency: It refers to the knowledge representation system's ability to cope with current knowledge in order to make room for new knowledge.  
• Adequate representation: A knowledge representation system's primary assets are adequacy and the capacity to make the AI system comprehend, which means it represents all of the knowledge required to govern a certain sector or topic.
• Inference effectiveness: The system of representation cannot contain new information in the presence of previously acquired old knowledge, but it may incorporate this knowledge effectively and without interference. 
• Acquisition effectiveness: The ultimate quality of the knowledge representation system is its capacity to autonomously learn new information, allowing AI to integrate into its current knowledge and become more efficient and productive as a consequence.

Information representation is the technique by which artificial intelligence conveys information not through stored data in the system but through previous experiences and knowledge in order to act intelligently like a person. Do you understand what distinguishes humans from machines? Intelligence? No, since that is precisely what AI does - it replicates human intelligence. Our conscience (the total of all our knowledge thus far), or the ability to think and reason, is one trait that differentiates humans from machines. Humans rely on this ability to perform all of our daily activities. We are aware, for example, that touching a hot pan may cause our hands to burn even before we touch it. This is the sophisticated way the human mind works, and if we want to communicate this complicated knowledge to a computer, we need to supply AI with more advanced information, which led to the concept of Knowledge Representation in AI.

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