The principle of intelligent agent technology is mainly based on machine learning and optimization algorithms.

Machine learning is a way for machines to learn from data, while optimization algorithms are a way to find the best solution.

AI agents learn patterns from data through machine learning and find optimal decisions through optimization algorithms.

The workflow of an AI Agent typically includes the following steps: Sensing the environment: The AI Agent obtains information about the environment through sensors or other means.

Understanding the environment: AI agents understand the environment information through machine learning algorithms.

Making decisions: The AI Agent makes decisions through optimization algorithms.

Execute decisions: The AI Agent executes decisions, such as moving the location or changing the state.

Learning and optimization: The AI Agent learns and optimizes its decisions based on outcomes.

In addition, for specific implementations, various core algorithms may need to be involved, such as machine learning algorithms and optimization algorithms.

Machine learning algorithms mainly include supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.

Among them, reinforcement learning is one of the most commonly used learning methods for AI agents, which learns optimal strategies through interaction with the environment.

The optimization algorithms mainly include gradient descent method, Newton method, stochastic gradient descent method, genetic algorithm and so on.

Among them, gradient descent method is the most commonly used optimization method, which finds the minimum value of the function by calculating the gradient of the function.

I hope the above content is helpful to you, you can consult a computer professional or consult related books for more information.