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.
It also explains how knowledge can be represented in artificial intelligence. Knowledge representation allows an intelligent computer to learn from its experiences and knowledge to behave intelligently, much like a person. It goes above simply putting data into a database.
There are Two Primary Concepts in Knowledge Representation
1. Knowledge
Knowledge is the state of having acquired information through learning and experience, which endows the agent with familiarity.
A computer using artificial intelligence will only carry out a particular action condition if it has accumulated experience from the past.
For example. Only after an AI agent has acquired enough knowledge to solve the chessboard puzzle and win the game can it solve it.
2. Representation
The objectives that are used to convey the knowledge required to address a particular issue make up representation.
The various types of knowledge that AI systems must represent are as follows:
- Object: All data is associated with items that exist in our world. For example, cars have wheels, buses require drivers, etc.
- Event: The various things that happen in our world all the time and how people interpret them. for example, victories and wars.
- Performance: Examines how people behave in different contexts. For the agent to comprehend the behavior side of knowledge, this knowledge must be represented.
- Facts: Information derived from an accurate depiction of our reality.
- Meta Knowledge: Meta knowledge is the understanding of what we already know and how to make AI see it as well.
Knowledge Base:
A knowledge base is an archive of data associated with any field of study. For instance, a database on road construction expertise.
Importance of Knowledge Representation in AI
- It simplifies the collection and integration of data, enhancing data management.
- Knowledge Representation maintains current information to guarantee correctness and applicability.
- Knowledge Representation collects insightful comments for improving services and products.
- It monitors performance indicators to support ongoing development. guarantees uniformity throughout the business, improving client experiences.
- Knowledge Representation provides real-time information for well-informed decision-making and draws insights from data.
Knowledge Representation Types in AI
1. Declarative knowledge
It's all about ideas and facts that serve to simplify things.
2. Structural Knowledge
This information facilitates problem-solving by helping AI comprehend the connections between concepts and things.
3. Procedural Information
This functions as a kind of work manual with detailed rules and strategies to keep to.
4. Metaknowledge
It is knowledge about knowledge, which includes prior learning, plans, and categories.
5. Heuristic Knowledge
This kind of helps AI in making judgments by drawing on prior knowledge, such as applying well-established methods to novel situations.
Approaches to Knowledge Representation in AI
Basic Relational Knowledge
This is similar to the clean column fact organization frequently found in databases. It's simple to understand, but not very useful for making conclusions. This can be used, for example, in a database to list relationships between individuals and their addresses.
Inheritable knowledge
This is where data is kept in a family tree-like hierarchy. This can be used, for instance, to illustrate the relationships between animals and different species or the classification of items. It is superior to the straightforward relational approach and aids in our understanding of how things relate to one another.
Inferential Knowledge
The exact method of applying formal logic to ensure accurate facts and conclusions is known as inferential knowledge. This can be used, for example, to derive that "Socrates is mortal" if "All men are mortal" and "Socrates is a man."
Procedural Knowledge
AI carries out tasks by utilizing little programs or rules. For instance, it can diagnose illnesses and play chess by following the rules. For specific jobs, it works well despite its drawbacks.
