For anyone interested in artificial intelligence (AI), understanding how these systems encode knowledge is crucial information. Knowledge representation is an essential part of artificial intelligence because it deals with how data is organized, stored, and used by computers to simulate human decision-making and cognitive processes. To provide readers with a clear and succinct overview, this article will cover the foundations of Knowledge Representation in AI.
What is Knowledge Representation?
In AI, the term “knowledge representation” refers to how knowledge is represented. In essence, it is the study of how to represent an intelligent agent’s beliefs, intentions, and judgments in a way that is appropriate for automated reasoning. An agent’s ability to behave intelligently is one of the main goals of knowledge representation.
Knowledge Representation and Reasoning (KR, KRR) is the process of representing real-world information so that a computer can comprehend it and use its knowledge to solve complicated real-world problems, such as natural language communication with humans. In artificial intelligence, knowledge representation goes beyond simply putting information in a database; it enables a machine to absorb knowledge and act rationally, much like a human.
Different Types of Knowledge
Heuristic Knowledge: This indicates a certain level of subject-matter expertise.
Declarative Knowledge: In a declarative sentence, it expresses ideas, facts, and objects.
Meta Knowledge: Knowledge about other kinds of knowledge is defined as meta-knowledge.
Structural Knowledge: It is fundamental knowledge for solving problems that explain the connection between ideas and things.
Procedural Knowledge: This is in charge of understanding how to perform a task and comprises guidelines, tactics, protocols, etc.
The Relation Between Knowledge and Intelligence
Real-world knowledge is essential to intelligence, and it is also necessary to create artificial intelligence. Knowledge is essential for AI agents to demonstrate intelligent behavior. Only when an agent has knowledge or experience with input can he act on it correctly?
Imagine that you were to meet someone who spoke a language you don’t know. How would you respond to that? This also holds for the agents’ intelligent behavior. The diagram below illustrates how a single decision-maker uses knowledge and environmental sensing to act. However, it cannot exhibit intelligent behavior if the knowledge component is absent.
Requirements for Knowledge Representation system
Acquisitional efficiency: the capacity to quickly pick up new information through automated means.
Representational Accuracy: The KR system ought to be able to represent every type of necessary knowledge.
Inferential Adequacy: To generate new knowledge that corresponds to the existing structure, the KR system should be able to manipulate representational structures.
Inferential Efficiency: the capacity to store relevant guides and use them to steer the inferential knowledge mechanism in the most fruitful directions.
Approaches to Knowledge Representation in AI
Simple Relational Knowledge: It is the most straightforward method of using the relational approach to store facts. Here, every detail regarding a set of objects is methodically arranged in columns. Additionally, database systems that represent the relationships between various entities are well-known for using this method of knowledge representation. As a result, there is limited room for deduction.
Example:
| Name | Age | Emp ID |
| John | 25 | 100071 |
| Amanda | 23 | 100056 |
| Sam | 27 | 100042 |
Inheritable Knowledge: Under the inheritable knowledge approach, all data needs to be organized either hierarchically or in a generalized form, and it needs to be stored in a hierarchy of classes. Additionally, this method includes inheritable knowledge known as instance relation, which illustrates the relationship between instance and class. This method uses boxed nodes to represent values and objects.
Inferential Knowledge: Formal logic serves as a representation of knowledge in the inferential knowledge approach. It can thus be applied to obtain additional facts. Additionally, accuracy is guaranteed.
Conclusion
Understanding knowledge representation in AI is critical for developing systems that mimic human intelligence. Artificial intelligence (AI) is capable of solving complex problems, communicating naturally, and performing intelligent tasks through efficient data organization and utilization. Heuristic, declarative, meta, structural, and procedural knowledge are essential components that enhance the overall capability of artificial intelligence (AI) systems.