Knowledge representation

Knowledge Representation in AI is a study of how the beliefs, intentions, and judgements of intelligent agent Basically, it describes the representation of knowledge. it can be expressed suitably for automated reasoning, one of the primary purposes of knowledge representation includes modelling intelligent behaviour for an agent.

knowledge representation in AI is not just storing the data in database. It allows machines to learn from knowledge and behaves intelligently like a human being. Knowledge representation and reasoning (KR, KRR) represents information from the real world for a computer to understand and then utilize this knowledge to solve complex real-life problems like communicating with human beings in natural language.

The different kinds of knowledge that need to be represented in Ai include:

  • • Objects
  • • Events
  • • Performance
  • • Facts
  • • Meta-Knowledge
  • • Knowledge-base

There are five different types of knowledge these are:

1. Declarative Knowledge - It includes concepts, facts and objects and expressed in a declarative sentence.

2. Structural knowledge - It is a basic problem-solving knowledge that describes the relationship between concepts and objects.

3. procedural Knowledge - This is responsible for knowing how to do something and includes rules, strategies, procedures, etc.

4. Meta knowledge - Meta knowledge defined knowledge about other types of knowledge.

5. Heuristic Knowledge - This representation some experts knowledge in the field or subject.

Cycle of knowledge representation in AI

Artificial Intelligent systems usually consists of various components to display their intelligent behaviour. Some of these components include:

  • • Perception
  • • Learning
  • • Knowledge Representation & reasoning
  • • planning
  • • ExecutionS

The above diagram shows the interaction of an AI system with the real world and the components involved in showing intelligence.

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