Knowledge representation in AI
Knowledge Representation and Reasoning in AI

Human beings in this world perform a lot of actions in their daily life. In short, these actions are based on their understanding, reasoning and thinking. Similarly, in AI, Knowledge Representation and Reasoning is applied to help machines perform reasoning and execution.

Knowledge Representation

It is the study of how reasons can be provided using the beliefs, aims and judgments. Above all, it includes the process of understanding, designing and executing. Therefore, representing information in computers. The programs can use this information later to work, learn, think and behave like a human being.

Knowledge Representation includes findings from psychology about how humans solve problems. Likewise, it also includes findings from logic to gain answers, such as in mathematics. In the real world, knowledge plays a very important role in intelligence as well as creating artificial intelligence. The AI system may act suitably on any input only if it has the knowledge or experience on said input.

Knowledge representation and reasoning poster.

Knowledge Reasoning

With the help of the information provided from the outside world, the computer derives its own information in the form of reasoning. After that, this information is used to solve complex real-life problems and make reasons. In this way, the computer can gain more new knowledge. Knowledge representation is useless without the ability of reasoning with them.

One of the main purpose to represent knowledge is to be able to reason with the knowledge. Therefore, knowledge Representation goes hand in hand with automated reasoning.

AI knowledge cycle

An AI system has the following components to display intelligent behavior:

  • Perception– Connecting with the real world and receiving further information from its surroundings.
  • Learning– This component is mainly responsible for learning from the data received from the Perception component.
  • Knowledge Representation and Reasoning– Involved in showing the intelligence. These two components are independent yet coupled together.
  • Planning
  • Execution

The planning and execution depend on analysis of Knowledge Representation and Reasoning.

AI knowledge cycle
AI knowledge cycle

The knowledge included in AI are objects, events, performance, meta-knowledge, etc.

  • Objects – The facts of anything in the world. For example, lets take a musical instrument. The color of the instrument is the object.
  • Events – Anything that occurs in the world. For example, playing the musical instrument.
  • Performance – It describes behavior which involves knowledge on how to do things. For example, the musical instrument played in any way.
  • Meta-knowledge – What we know.

In solving problems in AI, we must include knowledge as information in two ways:

  • Facts – The truths about the world and what it represents. To sum up, it could be anything from the way it is to how it works.
  • Representation of the facts – The information which we manipulate and produce.
AI knowledge base and Reasoning engine
AI knowledge base and Reasoning engine

For an AI system to work as humans, we need to give data in a knowledge base and a reasoning engine. The knowledge base contains facts and general knowledge about any particular things in the world. On the other hand, a reasoning engine produces and derives consequences and results. This is done by studying from the data already in the knowledge base.

Applications of Knowledge Representation and Reasoning

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