人工智能ai 學習

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.

學習是人類行為的重要組成部分。 這是任何人類發展階段的第一步。 當提出人工智能的概念時，開發人員的主要方法是建立一個系統，該系統可以在不同情況下作為人類做出React，并可以在學習，推理和解決問題方面模仿人類的行為。 因此，學習是構建專家系統或任何可在人工智能上運行的系統的基礎且非常重要的部分。

Why do we want our agent to learn?

為什么我們要我們的代理商學習？

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.

在應對未知環境時，學習非常重要。 在構建代理程序時，我們可以提供信息和解決方案，以解決在構建初期我們所知道的問題，但是我們不知道代理程序可能會隨著時間面臨什么樣的問題。 因此，必須將學習因素包括在系統中，以便代理可以進行自我訓練并改善和更新其知識庫。 通過這樣做，代理變得自力更生，并且開發人員或用戶不需要一次又一次地將信息提供給代理。 代理現在可以自我分析問題并從周圍環境中學習。 這樣可以提高代理的性能并增強其決策機制。

How the agent learns from its surroundings?

代理如何從周圍的環境中學習？

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.

代理通過其傳感器實現學習部分。 根據條件，代理可以找到問題的解決方案并做出決策。 然后，它觀察這些決策的結果，并從中了解決策是否正確，還是有待改進。 因此，下一次代理人遇到類似問題時，它將以先前的解決方案為參考，并在這次做出更好的決策。 除此之外，代理還通過學習其周圍環境中可能導致代理環境發生任何變化的各種活動來不斷改進其知識庫。

翻譯自: https://www.includehelp.com/ml-ai/learning-agents-artificial-intelligence.aspx

人工智能ai 學習

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