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+# 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