- Artificial Intelligence Tutorial
- AI - Home
- AI - Overview
- AI - History & Evolution
- AI - Types
- AI - Terminology
- AI - Tools & Frameworks
- AI - Applications
- AI - Real Life Examples
- AI - Ethics & Bias
- AI - Challenges
- Branches in AI
- AI - Research Areas
- AI - Machine Learning
- AI - Natural Language Processing
- AI - Computer Vision
- AI - Robotics
- AI - Fuzzy Logic Systems
- AI - Neural Networks
- AI - Evolutionary Computation
- AI - Swarm Intelligence
- AI - Cognitive Computing
- Intelligent Systems in AI
- AI - Intelligent Systems
- AI - Components of Intelligent Systems
- AI - Types of Intelligent Systems
- Agents & Environment
- AI - Agents and Environments
- Problem Solving in AI
- AI - Popular Search Algorithms
- AI - Constraint Satisfaction
- AI - Constraint Satisfaction Problem
- AI - Formal Representation of CSPs
- AI - Types of CSPs
- AI - Methods for Solving CSPs
- AI - Real-World Examples of CSPs
- Knowledge in AI
- AI - Knowledge Based Agent
- AI - Knowledge Representation
- AI - Knowledge Representation Techniques
- AI - Propositional Logic
- AI - Rules of Inference
- AI - First-order Logic
- AI - Inference Rules in First Order Logic
- AI - Knowledge Engineering in FOL
- AI - Unification in First Order Logic (FOL)
- AI - Resolution in First Order Logic (FOL)
- AI - Forward Chaining and backward chaining
- AI - Backward Chaining vs Forward Chaining
- Expert Systems in AI
- AI - Expert Systems
- AI - Applications of Expert Systems
- AI - Advantages & Limitations of Expert Systems
- AI - Applications
- AI - Predictive Analytics
- AI - Personalized Customer Experiences
- AI - Manufacturing Industry
- AI - Healthcare Breakthroughs
- AI - Decision Making
- AI - Business
- AI - Banking
- AI - Autonomous Vehicles
- AI - Automotive Industry
- AI - Data Analytics
- AI - Marketing
Artificial Intelligence - Knowledge Representation
Knowledge Representation (KR) plays an essential role in artificial intelligence that enables systems to organize and interpret information in a way similar to human thinking.
This allows AI systems to process data, make decisions, and tackle problems by keeping knowledge in a structure form. Just as humans use language or symbols to express thoughts, AI needs structures to represent the world around it.
What is Knowledge Representation in AI
Knowledge Representation in AI uses the methods and frameworks to encode and store knowledge making it accessible to reason and make decisions. It allows machines to process and use information to understand the world, solve problems, and learn from experiences.
KR has an impact on simplifying and structuring large amounts of complex data, which makes it easier to analyze and apply AI.
Good knowledge representation can boost machine learning by offering improved data structures that the system can employ to learn and recognize patterns.
Knowledge representation frameworks can be used in different problem areas, like healthcare, robotics, or finance letting AI work in various contexts.
By arranging knowledge well, KR helps make decisions faster and with more information.
What to represent in AI
Following are the kind of knowledge which needs to be represented in AI systems −
Object Knowledge: Information about physical objects and their properties, for example, "A laptop has keyboard, mouse, screen". or "A tree has branches, leaves, and roots. "This helps AI recognize and classify things in its environment.
Event Knowledge: Knowledge about actions or events, for example, "A traffic light turns red" or "A user clicks a button." This helps AI understand cause and effect.
Performance Knowledge: Knowledge of how to do tasks, like "How to Bake a Cake" or "How to Troubleshoot a Machine." It gives AI the efficiency to accomplish tasks.
Meta Knowledge: Knowledge of what the system knows, such as understanding when to update the knowledge base or determining which information is most useful in a given context.
Factual Knowledge: Verifiable statements or facts, such as "Water boils at 100C." It serves as the basis of AI reasoning and decision-making.
Knowledge base: A centralized source of maintaining facts, rules, and procedures, which enables the AI system to take decisions and address problems based on relevant knowledge.
Relation between Knowledge and Intelligence
Knowledge and intelligence are important concepts in Artificial Intelligence. Knowledge gives the facts and information needed for reasoning and to solve problems, while intelligence use that knowledge to fix problems, decide things, and adjust to new situations. An AI system that has more knowledge can seem more intelligent by making smart choices based on what it knows.
Knowledge without intelligence is nothing but having a raw information without the ability to use them while intelligence without knowledge means lacking the information to make smart decisions.
Knowledge allows AI to assess different options and make decisions on past information and learned patterns.
Example: Online stores use what customers have looked at before to suggest products.
An intelligence makes an AI understand and implement knowledge in many different situations, therefore making it an accomplishment machine.
Types of Knowledge in AI
Following are the various types of Knowledge −
Declarative Knowledge
Declarative knowledge refers to facts, statements, or information that describe "what is known" about a domain. It can be described as static since the information can be represented as either assertions or truths.
For example, declarative knowledge has facts like "The sky is blue", "Delhi is capital of India", and "A triangle has three sides". These statements are declarative knowledge because it is a fact that can be directly expressed and documented.
Declarative knowledge contains various facts and knowledge regarding the world.
It answers "what" queries rather than "how" to achieve anything.
This type of knowledge is easy to convey through statements, databases, or documents.
Examples include scientific truths, historical events, and general knowledge.
AI uses declarative knowledge for reasoning, decision-making, and problem-solving.
AI Application: In a question-and-answer system, declarative knowledge is utilized to answer factual questions such as "What is the capital of France?"
Procedural Knowledge
Procedural knowledge refers to the knowledge, of how to perform a task. It involves procedures, methods, or processes that needed to achieve a task or solve a problem. It focuses on step-by-step procedures rather than just facts.
For example, solving a quadratic equation is an structured process that includes determining coefficients, applying the quadratic formula, and simplifying the result.
Procedural knowledge gives step-by-step instructions for how to do a task.
It explains "how" to do something rather than just stating the facts.
This knowledge is acquired through practice and experience.
Procedural knowledge more difficult to convey explicitly than declarative knowledge.
It is widely used in artificial intelligence applications like automation, robotics, and expert systems.
AI Application −
In robotics, procedural knowledge is applied when programming a robot to perform duties such as assembling auto-mobiles or navigating in a maze.
A robot chef follows rules in a step-by-step manner to prepare a meal. "How To Make Tea: Boil water. Add tea leaves. Pour into a cup. Add sugar and milk to taste."
Meta Knowledge
Meta-knowledge is a term that refers to "knowledge about knowledge." It allows AI to understand what it knows, how reliable the information is, and when to apply it. This kind of knowledge allows AI systems to evaluate and enhance their reasoning and decision-making abilities.
For instance, if an AI chatbot knows that its answers are sourced from a reliable database, it will become more confident in its responses.
Meta-knowledge is the capability of AI to evaluate the correctness and reliability of its own knowledge.
It enables AI to determine whether it should use specific rules or facts.
Crucial in learning, over time it sharpens the artificial intelligence decision to make.
It is very useful for debugging AI models as it helps detecting gaps or inconsistencies.
Meta-knowledge improves problem-solving by helping AI in choosing the best reasoning strategy.
AI application, a self-driving auto-mobile knows traffic laws and has meta-knowledge to recognize false sensor data caused by fog. It can then decide whether to slow down or switch to a backup system.
Heuristic knowledge
Heuristic knowledge is the rule-of-thumb or experience-based knowledge that helps in problem-solving and decision-making when complete information is not available. It helps in decision-making when specific rules or formulae are not available.
Heuristic knowledge often relies on intuition, experience, or common sense.
This knowledge is used in AI to make accurate and quick decisions, particularly in complex or unclear situations.
Heuristic information reduces computation time by directing the search process toward likely solutions.
It is widely used in applications such as game AI, medical diagnostics, and optimization problems where exact answers are hard to compute.
AI Application: In game-playing AI like chess or Go, heuristic knowledge helps the machine to evaluate board positions and make strategic decisions.
Structural Knowledge
Structural knowledge refers to the relationships and connections between different concepts or things in a domain. It helps AI understand how things are related.
Structural knowledge describes how things are structured and connected.
This knowledge is often represented as graphs, trees, or networks.
Structural knowledge is widely used in many applications of artificial intelligence, for example, in semantic networks, ontologies, and knowledge graphs.
This type of knowledge assists AI in inferring new relationships from the existing ones which improves future decision making process quick and accurate.
Structural knowledge strengthens reasoning and decision-making by presenting information in an organized manner.
For example, an AI-driven medical diagnosis system understands, A fever is a symptom of flu. Flu is caused by a viral infection. Antiviral drugs can treat viral infections this way it connects different entities in a domain.