# Solving Problems by Searching

Whenever we need to solve a problem, our `agent` will need to be able to foresee
outcomes in order to find a `sequence` of `actions` to get to the `goal`.

Here our `environment` will always be:

- Episodic
- Single-Agent
- Fully Observable
- Deterministic
- Static
- Discrete
- Known

And our `agents` may be:

- Informed: when they know how far they are from the objective
- Uninformed: when they don't know how far they are from the objective

## Problem-Solving agent

This is an `agent` which has `atomic` representations of states.

### Problem-Solving Phases

1. Formulate `Goal`
2. Formulate problem with adequate *abstaction*
3. Search a solution ***before*** taking any action
4. Excute plan

With these 4 phases the `agent` will either come to a `solution` or
that there are ***none**.

Once it gets a `solution`, our `agent` will be able to ***blindly execute*** its
action plan, as it will be ***fixed*** and thus, the `agent` won't need to perceive
anything else.

> [!NOTE]
> This is also called **Open Loop** in Control Theory

> [!CAUTION]
> The 3rd and 4th step can be done only in this `environment` as it is `fully-observable`, `deterministic` and `known`,
> so the `environment` can be predicted at each `step` of the **searching simulation**.

## Planning Agent

This is an `agent` which has `factored` or `structures` representation of states.

## Search Problem