Knowledge Representation Schemes in Artificial Intelligence (AI)

Knowledge Representation

The area of artificial intelligence that deals with knowledge representation and reasoning is what helps AI agents think and how thinking impacts their intelligent behavior. 

Its job is to express real-world information in a way that computers can understand and apply to solve complicated real-world issues, such as diagnosing a medical condition or having natural language conversations with people.

Knowledge Representation Schemes in Artificial Intelligence (AI)

The internal, conceptual, and external components of a knowledge base are all specified in different schemas and combined to generate a formal specification known as a knowledge representation schema. 

A knowledge base's external schema formalizes its practical language, its conceptual schema specifies concepts, and its internal schema describes the organization of the knowledge base.

Therefore, the knowledge representation schemes have been categorized simply as follows:

• Declarative representation schemes

• Procedural representation schemes

• Meta-Knowledge

• Heuristic Knowledge

1. Declarative Representation Schemes

A declarative knowledge definition would be the knowledge of facts and specific points of information.

Declarative knowledge is derived from concepts, methods, processes, and other such things. It declares all known information and allows the reasoning system to derive new information by using the rules of inference.

Production systems are a representation of declarative knowledge. It is focused on facts. It is easily communicable. It revolves around the 'WHAT' of the concept.

The following example clarifies declarative knowledge representation.

ROBOT (x) = MORTAL (x)

PERSON (x) = MORTAL (X)

ROBOT (AI MACHINE)

PERSON (RAJU)

That illustrates a few basic truths, including that Raju is both a human and a robot/AI machine and that all robots and people are mortal. The algorithm may automatically determine that since is mortal, so is every bird by applying modus ponens.

Advantages of Declarative Knowledge Representation

• Declarative programming removes the low-level logic and details from your code, making it simpler.

• It focuses on the overarching objectives and limitations.

• Additionally, it improves your code's readability and comprehension by succinctly and explicitly expressing goals without causing unintended consequences or changing states.

• Clarity and transparency in the knowledge that is expressed

• Adaptability in the storing of knowledge. 

• Every bit of knowledge is a stand-alone, autonomous unit. Modularity is hence higher.

The following are the typical means for declarative knowledge represented

• Formal Logic

• Semantic Network

• Frames

• Scripts

2. Procedural Representation Scheme

One category of knowledge called procedural knowledge is that which deals with knowing how to perform an action. It is directly applicable to all tasks. Another name for it is imperative knowledge. It consists of policies, plans, schedules, and other things. The task for which procedural knowledge can be applied determines its application.

Unlike declarative knowledge, which can be picked up quickly, procedural knowledge typically requires time and practice to perfect. On the other hand, procedural knowledge proves that you are capable of physically operating a vehicle and observing traffic signals. However, acquiring this expertise requires consistent practice. You get more proficient at this ability the more you drive.

For example: Those who ride bicycles gain expertise and practice in balancing, steering, and pedaling.

Advantages of Procedural Representation Scheme

• Easy control of the inference process.

• Heuristic knowledge can be easily represented which is vital.

• One has control over search which is not available in declarative Knowledge representation.

• It is easy to code.

• It is directly applied to a task.

• It offers the abilities and methods required to effectively navigate complex difficulties.

• People who are knowledgeable about procedures can work more productively and finish tasks faster and with less effort and time.

3. Meta-Knowledge

In the area of artificial intelligence, the term "meta-knowledge" refers to the understanding of previously established knowledge. Meta knowledge includes various aspects such as learning, tagging, and planning. 

This model has a distinct specification and changes over time. Knowledge engineers may employ meta-knowledge, which includes things like accuracy, relevance, evaluation, prediction, comprehensiveness, confusion, justification, duration of life, purpose, source, and reliability.

4. Heuristic Knowledge

This information, which follows the rule of rule, is sometimes referred to as basic understanding. Because it solves problems using records of prior difficulties or problems accumulated by professionals, it is incredibly effective at reasoning. It provides ideas based on the issues that it has previously resolved.