A Practical Guide to Knowledge Representation in AI (Artificial Intelligence)

Summary: Machines can learn and think like humans! It seems fascinating to see that machines make intelligent decisions without human intervention. But how can they do it accurately? This question buzzes our minds. That’s where Artificial intelligence is making its winning place. But that’s not all! There is something more: knowledge representation in AI. It can help AI systems with vast knowledge or provide sensory inputs to perceive, learn, plan, and execute things. Let’s enter the knowledge representation of the Artificial intelligence world and know how this magic will work!!

Envision a world where machines have the ability to think, learn and make smart decisions like humans.

It may sound like a distant dream, but in reality, it’s a technological revolution. More than that, it’s a paradigm shift that has challenged conventional ways of thinking, creativity, and intelligence. This can happen due to the emerging Artificial technology.

AI systems can compute information correctly and communicate like humans if they can interpret, understand, and reason for that information. To make this happen, knowledge representation in AI can be a solution. It’s a part of AI which has AI agents who think, understands real complex problems, and learn from the information that has been stored in the databases.

What is Knowledge Representation in AI?

It is a process of curating the model in a structured way based on knowledge or information. This way, machines can easily understand the information and perform specific tasks such as reasoning, addressing and solving real-life problems, and making well-informed decisions.

In other scenarios, it can convert machine-readable information like symbols, frames, graphs, and Logic into real-world scenarios so that the machines can learn, understand, interpret, and utilize the information like humans.

The primary objective of knowledge representation in AI is to provide smart systems that can communicate like a human with the real world, drive innovation and improvise decision-making.

Knowledge Types Presented in AI

Five Fundamental Types of Knowledge

Here are five fundamental types of knowledge representation in AI. Let’s explore these- 

1. Declarative knowledge 

• This knowledge helps to depict our world and include a detailed description of things.  
• It is a kind of knowledge that incorporates facts, concepts, and objects.  
• It is simple compared to procedural language and usually explicit in a declarative language.  

2. Structural knowledge 

• It is quite simple and fundamental knowledge that is utilized to solve complex problems in the real world.  
• It defines the various objects and concepts’ relationships. 

3. Procedural knowledge 

• This type of procedural knowledge is also referred to as imperative knowledge.  
• It includes how things can work or perform in a certain way. For example, a mobile robot can create its own plan if it has given a building map.  
• To accomplish the tasks, this knowledge can specify processes, rules, strategies, agendas, etc. 

4. Meta knowledge 

• The predefined knowledge in the AI representation space is called meta-knowledge. Let’s say learning, planning, etc.  
• This type of knowledge can alter its behavior according to time while leveraging different specifications.  
• Let’s say a knowledge engineer can utilize meta-knowledge in different aspects such as accuracy, reliability, applicability, consistency etc. 

5. Heuristic knowledge 

• This type of knowledge representation in AI indicates expert knowledge in any field or in any subject they have acquired over the years.  
• It can collect past problem experiences and, based on that, suggest a key approach for that problem and take action.

Different Approaches to Knowledge Representation in AI

There are four types of processes that can describe the different approaches to knowledge representation in AI.  

1. Simple relationship knowledge 

• It leverages the relational method in which it can store facts systematically.  
• Arrange Each fact about a group of objects in columns systematically to easily retrieve and manage data.  

For Example, utilizing a relational database to store the data of the employees in the company- 

In this, each column represents a special attribute about the employee such as Name, age, department, and salary. Thus, it becomes easy to retrieve and manage the information.

2. Inheritable knowledge 

• In this type of approach, the data are arranged in a hierarchy of classes.  
• Each class is organized hierarchically.  
• This approach is also known as instant relation, which indicates the relation between class and instance. 

3. Inferential knowledge 

• This type of knowledge can be retrieved from facts.  
• In this approach, the knowledge can be presented in terms of explicit logic.  
• The inferior knowledge ensures correctness.  
• It uses several techniques, including logic-based reasoning, deep learning, and probability reasoning, to help make informed decisions. 

4. Procedural knowledge 

• It utilizes small codes and programs to help how things can work and how they are going to proceed.  
• The “If then” rule is the pivotal rule which is used in this approach.  
• LISP language and Prolog language can be leveraged by procedural knowledge.

The Cycle of Knowledge Representation in AI

Artificial intelligence incorporates a magnitude of components to work smartly and efficiently. Let’s plunge into some of the key system components 

• Perception 
• Learning 
• Knowledge representation 
• Planning 
• Execution 

1. Perception 

This type of component can fetch information or data from the environment. The information or data can be retrieved with the help of camera sensors or devices to perceive or analyze data (maybe sound or image). The primary objective of the perception component is to make the system understand or sense the environment. 

2. Learning 

The learning component enhances its overall performance over time from the data captured by the perception and keeps improvising its internal model. It can be on the journey of self-improvement where it can optimize its system behavior with the help of techniques and learning methods such as enforcement, supervised, unsupervised, etc., to learn new things. 

3. Knowledge representation 

It is one of the vital components in the cycle of knowledge representation in AI as it has the capability to make machines behave like humans and make intelligent decisions. In knowledge representation, the information is encoded by choosing the right representation scheme, including logic graphs, to organize and store data. When it comes to reasoning, is a process in which a system utilizes knowledge and makes decisions considering the criteria or rules to behave intelligently. 

4. Planning 

This component makes proper planning by considering the knowledge representation along with reasoning. It can create a set of decisions for actions in order to reach the dedicated goals. It can considerate the magnitude of aspects, including resources, time frame, and surrounding environment, to make a well-informed and smart decision. 

5. Execution 

Now is the time to take the primary action depending on the internal model of the system and other factors such as sensory input. To attain the final outcome, the execution component can seamlessly control and communicate with the other system components.

Properties of Knowledge Representation in AI

A magnitude of properties indicates a good knowledge representation of AI systems. Let’s look at some of the properties of knowledge representation in AI. 

1. Inferential adequacy 

It indicates how flexible the knowledge representation system can effectively accord with the current knowledge, which can help create a path for new knowledge.  

2. Representational adequacy 

This type of education helps the AI system understand the necessary knowledge representation assets, which can further help manage specific fields.  

3. Inferential efficiency 

The capability to lead the inferential knowledge in the most effective and right directions with the help of storing relevant guides without hassle.  

4. Acquisitional adequacy 

Harness the automatic methods to integrate the existing knowledge and acquire new knowledge so as to produce the more productive and best outcomes. 

Knowledge and Intelligence Connection

When it comes to intelligence, knowledge is a vital element. Artificial intelligence systems can perform accurately when they acquire knowledge or experience from the environment. And if there is not enough knowledge that decisions made by AI systems can be no-to-less relevant.

Let’s say a doctor does not have relevant knowledge of medical treatment symptoms and conditions. Then how can a doctor make an intelligent decision? But if a doctor has sufficient knowledge to diagnose and give the proper treatment, the recovery of the patient is likely to happen in a positive way.

Similarly, the intelligent behavior shown by the AI agents would be possible if the system has sufficient knowledge and how they are going to apply it. Basically, knowledge representation in AI can indicate that a system can show intelligent behavior if it has enough knowledge or experience of the environment.

Conclusion: Let’s Wind Up

Better than today: Artificial intelligence systems can be performed well if they accurately interpret their environment and, based on that make decisions. Knowledge representation in AI can play a pivotal role in achieving the journey of being better than today. It enables machines to perform tasks, address problems, solve them, and make smart decisions. Various methods and techniques are available in knowledge representation to help AI systems retrieve and learn information.