犹他大学CS 6300：Artificial Intelligence

课程主页：https://dsbrown1331.github.io/intro-ai-class/

基本不出所料，这本课程拷贝了Berkeley CS188。

https://inst.eecs.berkeley.edu/~cs188/su25/

Office hours: https://docs.google.com/spreadsheets/d/1l8_Ao3nhnRuwWtWc2FHGDa45U35HzWy-PXrfO-VEAh4/edit?gid=0#gid=0

Piazza: https://piazza.com/class/me9fkw623gc52m

第一章：绪论

An agent is just something that acts.
A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.

The rational-agent approach to AI has prevailed throughout most of the field’s history. In the early decades, rational agents were built on logical foundations and formed definite plans to achieve specific goals. Later, methods based on probability theory and machine learning allowed the creation of agents that could make decisions under uncertainty to attain the best expected outcome.

In a nutshell, AI has focused on the study and construction of agents that do the right thing. What counts as the right thing is defined by the objective that we provide to the agent. This general paradigm is so pervasive that we might call it the standard model.