Knowledge representation in Artificial Intelligence (AI)
In this page we will learn about What is knowledge representation in Artificial Intelligence (AI) | What is knowledge representation | What to Represent | Types of knowledge | Declarative Knowledge | Procedural Knowledge | Meta- knowledge | Heuristic understanding | Structural knowledge | The relation between knowledge and intelligence | AI knowledge cycle | Approaches to knowledge representation | Simple relational knowledge | Inheritable knowledge | Inferential knowledge | Procedural knowledge | Requirements for knowledge Representation system
What is knowledge representation?
Humans excel in comprehending, reasoning, and interpreting information. Humans have knowledge about things and use that knowledge to accomplish various activities in the real world. However, knowledge representation and reasoning deal with how robots achieve all of these things. As a result, the following is a description of knowledge representation:
- Knowledge representation and reasoning (KR, KRR) is a branch of artificial intelligence that studies how AI agents think and how their thinking influences their behavior.
- It is in charge of describing information about the real world in such a way that a computer can comprehend and use it to solve difficult real-world problems such as diagnosing a medical ailment or conversing in natural language with humans.
- It's also a means of describing how artificial intelligence can represent knowledge. Knowledge representation is more than just storing data in a database; it also allows an intelligent machine to learn from its knowledge and experiences in order to act intelligently like a person.
What to Represent:
The types of knowledge that must be represented in AI systems are as follows:
- Object: All of the information on objects in our domain. Guitars, for example, have strings, while trumpets are brass instruments.
- Events: Events are the actions that take place in our world.
- Performance: Performance is a term used to describe behavior that entails knowing how to perform things.
- Meta-knowledge: Meta-knowledge is information about what we already know.
- Facts: The truths about the real world and what we represent are known as facts.
- Knowledge base: The knowledge base is the most important component of knowledge-based agents. It's abbreviated as KB. The Sentences are grouped together in the Knowledgebase (Here, sentences are used as a technical term and not identical with the English language).
Types of knowledge
The various types of knowledge are as follows:
1. Declarative Knowledge:
- Declarative knowledge is the ability to understand something.
- It contains ideas, facts, and objects.
- Declarative sentences are used to express descriptive knowledge, which is also known as descriptive knowledge.
- It is less complicated than procedural programming
- Games are modeled as a Search problem and a heuristic evaluation function, which are the two primary variables that aid in the modeling and solving of games in AI.
2. Procedural Knowledge:
- It's sometimes referred to as "imperative knowledge."
- Procedure knowledge is a form of knowledge that entails knowing how to do something.
- It can be used to complete any assignment.
- It has rules, plans, procedures, and agendas, among other things.
- The use of procedural knowledge is contingent on the job at hand.
Meta-knowledge is information about other sorts of knowledge.
4. Heuristic understanding:
- Heuristic knowledge is the sum of the knowledge of a group of specialists in a certain field or subject.
- Heuristic knowledge refers to rules of thumb that are based on prior experiences, awareness of methodologies, and are likely to work but not guarantee
5. Structural knowledge:
- Basic problem-solving knowledge is structural knowledge.
- It describes the connections between distinct concepts such as kind, part of, and grouping.
- It is a term that describes the relationship between two or more concepts or objects.
The relation between knowledge and intelligence:
Real-world knowledge is essential for intelligence, and artificial intelligence is no exception. When it comes to exhibiting intelligent behavior in AI entities, knowledge is crucial. Only when an agent has some knowledge or expertise with a given input can he act appropriately on it.
Consider what you would do if you encountered someone who spoke to you in a language you did not understand. The same can be said for the agents' intelligent behavior.
One decision maker, as shown in the diagram below, acts by detecting the environment and applying knowledge. However, if the knowledge component is missing, it will be unable to demonstrate intelligent behavior.
AI knowledge cycle:
For showing intelligent behavior, an artificial intelligence system must have the following components:
- Knowledge Representation and Reasoning
The diagram above depicts how an AI system interacts with the real environment and what components assist it in displaying intelligence. Perception is a component of an AI system that allows it to gather information from its surroundings. It can be in the form of visual, aural, or other sensory input. The learning component is in charge of gaining knowledge from the data collected by Perception comportment. The main components of the entire cycle are knowledge representation and reasoning. These two elements have a role in demonstrating intelligence in machine-like humans. These two components are independent of one another, but they are also linked. Analysis of knowledge representation and reasoning is required for planning and implementation.
Approaches to knowledge representation:
There are basically four approaches to knowledge representation, which are:
1. Simple relational knowledge:
It is the most basic technique of storing facts that use the relational method, with each fact about a group of objects laid out in columns in a logical order.
- It is the most basic technique of storing facts that use the relational method, with each fact about a group of objects laid out in columns in a logical order.
- This method of knowledge representation is often used in database systems to express the relationships between various things.
- This method leaves minimal room for inference.
Example: The following is the simple relational knowledge representation.
2. Inheritable knowledge:
- All data must be kept in a hierarchy of classes in the inheritable knowledge approach.
- All classes should be organized in a hierarchical or generic fashion.
- We use the inheritance property in this method.
- Other members of a class pass on their values to elements.g
- The instance relation is a type of inheritable knowledge that illustrates a relationship between an instance and a class.
- Each individual frame might indicate a set of traits as well as their value.
- Objects and values are represented in Boxed nodes in this technique.
- Arrows are used to connect objects to their values.
3. Inferential knowledge:
- Knowledge is represented in the form of formal logics in the inferential knowledge approach.
- More facts can be derived using this method.
- It ensured that everything was in order.
- Example: Let's suppose there are two statements:
- Marcus is a man
- All men are mortal
- Then it can represent as
∀x = man (x) ----------> mortal (x)s
4. Procedural knowledge:
- Small programs and codes are used in the procedural knowledge approach to specify how to do specific things and how to proceed.
- One significant rule employed in this method is the If-Then rule.
- We may employ several coding languages, such as LISP and Prolog, with this information.
- Using this method, we can readily represent heuristic or domain-specific information.
- But it is not important that we represent all the cases in this approach.
Requirements for knowledge Representation system:
A good knowledge representation system have to possess the following properties.
- Representational Accuracy: The KR system should be able to represent any type of knowledge that is necessary.
- Inferential Adequacy: The KR system should be able to change representational structures in order to generate new knowledge that matches the existing structure.
- Inferential Efficiency: The ability to store appropriate guides and steer the inferential knowledge process in the most productive ways.
- Acquisitive efficiency: The ability to quickly acquire fresh information utilizing automated means.
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