# Introduction

**Agent:** an entity that has goals or preferences and tries to perform a series of actions that yeild the best/optimal expected outcome given these goals.

**Percept sequence:** the complete history of content the agent has perceived.

**Environment:** the given instantiation of the agent.

**Environment type:**

* Fully vs Partially observable
* Deterministic vs. Stochastic

  The outcome of an action is completely determined by the state of the environment and the action taken by an agent.
* Episodic vs. Sequential

  Episodic environment can be divided into episodes which have no influence on each other

  Short-term actions do not have long-term consequences
* Static vs. Dynamic

  Environment doesn’t change while the agent is deliberating
* Discrete vs. Continuous

  The distinct states of the environmement is finite/infinite

> **Playing soccer**: Partially observable, stochastic, dynamic, sequential, continous
>
> **Shopping for used books on the internet**: Partially observable, stochastic, dynamic, ~~episodic~~ sequential

**World:** an environmant and the agents that reside within it create a world.

**Simple reflex agent**: one that doesn’t think about the consequences of its actions, but rather selects an action based solely on the current state of the world. **Example:** A basic thermostat used in a home to control the heating or cooling system.

**Model-based reflex agent:** maintains some internal state which depends on the percept history, useful if the current environment cannot be fully described by the current percept. **Example:** An autonomous robot vacuum cleaner recording the environment and navigates through the room.

**Utility-based agents:** compares the desirability of different environment states via a utility function. This allows the comparison of different goal states and action sequences and tradeoffs between different goals. **Example:** ADC.

**Goal-based agents**: makes decisions in order to achieve a set of predefined goals, in addition to maintaining internal state.

**Problem-solving agents:**

<figure><img src="https://p.ipic.vip/6ss42r.png" alt=""><figcaption></figcaption></figure>

**Single-state problem formulation:** complete observable

A problem is defined by four items: (1) Initial state. (2) Actions(or successor function $$S(x)$$). (3) Goal test. (4) Path cost (additive).

A solution is a sequence of actions leading from initial state to goal state.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://osh.fducslg.com/notes/readme/00-introduction.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.