Chapter 3 Initial Commit

This commit is contained in:
Christian Risi 2025-03-22 16:40:02 +01:00
parent b5b15edc65
commit 4ec407702d

View File

@ -0,0 +1,50 @@
# 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