人工智能ai 学习_学习代理| 人工智能

人工智能ai 学习

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.

学习是人类行为的重要组成部分。这是任何人类发展阶段的第一步。当提出人工智能的概念时，开发人员的主要方法是建立一个系统，该系统可以在不同情况下作为人类做出React，并可以在学习，推理和解决问题方面模仿人类的行为。因此，学习是构建专家系统或任何可在人工智能上运行的系统的基础且非常重要的部分。

Why do we want our agent to learn?

为什么我们要我们的代理商学习？

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.

在应对未知环境时，学习非常重要。在构建代理程序时，我们可以提供信息和解决方案，以解决在构建初期我们所知道的问题，但是我们不知道代理程序可能会随着时间面临什么样的问题。因此，必须将学习因素包括在系统中，以便代理可以进行自我训练并改善和更新其知识库。通过这样做，代理变得自力更生，并且开发人员或用户不需要一次又一次地将信息提供给代理。代理现在可以自我分析问题并从周围环境中学习。这样可以提高代理的性能并增强其决策机制。

How the agent learns from its surroundings?

代理如何从周围的环境中学习？

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.

代理通过其传感器实现学习部分。根据条件，代理可以找到问题的解决方案并做出决策。然后，它观察这些决策的结果，并从中了解决策是否正确，还是有待改进。因此，下一次代理人遇到类似问题时，它将以先前的解决方案为参考，并在这次做出更好的决策。除此之外，代理还通过学习其周围环境中可能导致代理环境发生任何变化的各种活动来不断改进其知识库。