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62 changes: 55 additions & 7 deletions content/5.Semester/CompInt/VL/3.VL/3.VL - CompInt.md
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Expand Up @@ -7,9 +7,12 @@ fach: "[[Computational Intelligence]]"
Thema:
Benötigte Zeit:
date created: Wednesday, 23. October 2024, 20:00
date modified: Thursday, 24. October 2024, 11:25
date modified: Friday, 25. October 2024, 14:32
---

_Start:_ Folie 80
_Ende:_ Folie 97

# Einführung und Orientierung

Willkommen zur Vorlesung über **Intelligente Agenten und deren Zielsetzungen**. Diese Vorlesung ist speziell für Informatikstudenten konzipiert und dient als Lernhilfe, ohne dabei übermäßig erschöpfend zu sein. Um den Überblick zu behalten, findet die Vorlesung auf drei parallelen Tracks statt:
Expand Down Expand Up @@ -89,6 +92,12 @@ Wir können komplexere Ziele definieren, z.B.:

### Zielsetzung

`gamma holds if the agent never executes action "do_nothing" twiche in a row`

$$
\gamma(\pi) \iff \overline{\exists}t: a_{t}= a_{t+1} = \text{"do\_nothing"}
$$

Ein häufiges Sicherheitskriterium ist, dass der Agent nicht gegen Wände läuft. Mathematisch ausgedrückt:

$$
Expand All @@ -103,6 +112,47 @@ Dies bedeutet, dass der Agent niemals die Aktion `Go North` ausführen darf, wen
- **Exploration**: Der Agent soll alle erreichbaren Felder innerhalb des Gitters erkunden.
- **Interaktion**: Der Agent soll mit Objekten innerhalb des Gitters interagieren (z.B. Objekte aufnehmen oder ablegen).

## 2.1 Resource / StockTrading

$$
\begin{aligned}
o^{*} &\in \mathscr{O} \\
o^{*}_{x}&=\text{"Reddit says buy"}+x\\
\end{aligned}
$$

$\gamma$ `holds iff everytime Reddit says,everyone should buy` $\alpha$ `stock, we buy 10 of that stock`

$$
\gamma(\pi)\iff\forall o_{t}\ \in \mathscr{O}: \exists x: o_{t}= o_{t}^{*}\implies (\text{'buy'},x,10) = \alpha_t
$$

### Possible Policy

`Nur ein Beispiel nicht komplett korrekt`

$$
\Gamma(o)=
\begin{cases}
(\text{'buy','Gamestop'},0), &\text{ if o = "Gamestop increase"} \\ \\
(\text{'sell','Gamestop'},1), &\text{ otherwise} \\
\end{cases}
$$

## 2.2 Personal Life Assistant

$\gamma$ `holds iff PLA recommends drinking water atleast six times a day`

$$
\mathscr{A}^{*} \in \mathscr{A} \text{so that } a^{*}\in \mathscr{A}^{*} \text{means "drinking water"}
$$

- Assume: 50 recommandations (=action) per day

$$
\gamma(\pi)\iff \forall t: \exists t^{'}\in [t \ ;\ t+50]:\:a_{t^{'}}\in\mathscr{A}^*
$$

## 3. Cutting Marble

### Szenario
Expand Down Expand Up @@ -199,7 +249,7 @@ Anstatt systematisch alle Policies zu durchlaufen, wählt Random Search zufälli
3. **Wiederholung**: Dieser Prozess wird für eine vorgegebene Anzahl von Versuchen (`n`) wiederholt.
4. \*\*R

ückgabe\*\*: Die erste Policy, die das Ziel erfüllt, wird zurückgegeben.
**Rückgabe**: Die erste Policy, die das Ziel erfüllt, wird zurückgegeben.

### Beispielcode

Expand Down Expand Up @@ -295,8 +345,6 @@ Robot

## Beispiel: Behavior Tree aus der Unreal Engine

![Behavior Tree Example](https://docs.unrealengine.com/4.26/en-US/InteractiveExperiences/BehaviorTrees/BehaviorTreeEditor/)

_Abbildung: Beispiel eines Behavior Trees aus der Unreal Engine._

## Vorteile
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51 changes: 51 additions & 0 deletions content/5.Semester/GridNCloud/Grid and Cloudcomputing.md
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---
date created: Wednesday, 30. October 2024, 16:13
date modified: Wednesday, 30. October 2024, 16:18
---

- [[2VL GridCloud-25-10-2024]]

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