Humans know things, which is known as knowledge. Every day, we execute multiple tasks and make numerous decisions using this knowledge. But how do machines think and carry out these tasks? A collection of data is used by machines. Knowledge representation is the capacity of machines to think and behave like humans, including understanding, interpreting, and reasoning.
The representation of knowledge is essential to artificial intelligence. The objective of knowledge representation, a subfield of artificial intelligence, is to provide real-world data in a way that computers can understand and use to address problems in the real world.
In this article, we are going to discuss knowledge representation in AI and how it helps machines to perform real-world tasks.
By learning from sets of information and experience that are readily available, knowledge representation enables machines to function and behave like humans. The ability of machines to imitate humans in tasks such as understanding and interpreting is linked to the creation of agents that ensure a pattern that contributes to machine behavior.
Hence, knowledge representation can be described as:
Several kinds of knowledge and set of data is needed by AI systems, which are:
There are five types of knowledge and are following:
Knowledge plays an important role in real intelligence as well as artificial intelligence. The act of an AI agent or system is directly dependent on knowledge and experience. A decision maker makes a decision by sensing the environment and using the knowledge it has. If we remove any one of them, it will not be able to display any intelligence.
An artificial intelligence system goes through many phases to display intelligent behaviour.
AI systems use a perception component to gather data from their surroundings. Any sensory kind may be used as input, including auditory and visual. The learning component is in charge of learning from the environmental data that is collected. The major components involved in displaying intelligence like humans are knowledge representation and reasoning components. The results of the preceding components influence the planning and execution components.
There are four main approaches to knowledge representation.
If executed properly, knowledge representation gives artificial intelligence systems the ability to act and behave like humans.
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