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Learning Agents | Artificial Intelligence

In this article, we are going to study the learning agents. We will discuss why we want an agent to have the learning factor. We will also study how the learning component is embedded in an agent and how an agent learns from its surroundings.
Submitted by Monika Sharma, on June 17, 2019

Learning is an important part of human behavior. It is the first step in the development phase of any human. When the concept of Artificial Intelligence was proposed, the main approach of the developers was to build a system which could react as humans in different situations and could imitate the human behavior in the aspects of learning, reasoning and problem-solving. So, learning is the fundamental and very important part of building an expert system or any system which works on Artificial Intelligence.

Why do we want our agent to learn?

Learning is very essential when dealing with the unknown environment. While building an agent, we can feed the information and solution to problems that are known to us at the initial stage of building, but we do not know what kind of problems the agent may face with time. So, the learning factor must be included in the system so that the agent can train itself and improve and update its knowledge base. By doing so, the agent becomes self-reliant and there is no need for the developer or the user to give the information to the agent again and again. The agent now has the capability to self-analyze the problems and learn from its surroundings. This improves the performance of the agent and enhances its decision-making mechanism.

How the agent learns from its surroundings?

The agent implements the learning part through its sensors. According to the conditions, the agent finds a solution to the problems and makes decisions. It then observes the outcome of those decisions and learns from them whether the decision made was right, or some improvements are still to be made in it. So, the next time whenever the agent confronts similar problems, it takes the previous solution as a reference and makes a better decision this time. Apart from this, the agent keeps improving its Knowledge Base by learning from the different activities taking place in its surroundings which are responsible for causing any change in the environment of the agent.






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