10 - Artificial Intelligence II [ID:9146]
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to either live Entonces or live in the moment,

whether it's life

or whatever you might have to study.

That's why we call this surface to November 24th.

Let's go to this Constellation A of history.

So you look at six primary priesties here,

and get back to, where's my utility base now?

No agent here.

We have, we do decision theory,

which is essentially taking a work model

and then selecting the best action.

And to just find out what the best actions are,

we looked at utilities.

Utilities as functions that basically take a state,

give you its desirability, and you want to maximize

the expected utility of anything you do.

And so the first question we talked about

was where do utilities come from?

And the answer is we can assess preferences of users

or we can just judge behavior of intelligent agents

and derive preferences from that.

Just by looking at their actions.

And if you not only have access to preferences

between material prizes,

but actually between prizes and lotteries

and lotteries and lotteries,

then you get a utility function.

And that is almost true.

The preferences have to obey the following set of rules.

One of the things I would like to,

which is why I'm briefly talking about this is,

my direction of preferences, the arrows,

is the opposite of Russell and Norwick.

So don't get confused.

I will at some point change the order in my slides.

Probably I'll do that after the exam,

so that you don't get confused and have a consistent way.

But if you're reading Russell and Norwick,

which you should do,

be careful that everything is turned around,

which is fine as long as you're aware what you're reading.

Okay, so the main result here is Ramsey's theorem

that basically says, if I have preferences

between prizes and lotteries and lotteries and lotteries,

then I can derive a utility function

up to linear transformations.

And then when I have this,

I can basically look at trying to optimize expected utility.

And that's what we've been doing.

We've been talking briefly about how to measure

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01:27:28 Min

Aufnahmedatum

2018-05-16

Hochgeladen am

2018-05-17 11:31:43

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en-US

Dieser Kurs beschäftigt sich mit den Grundlagen der Künstlichen Intelligenz (KI), insbesondere mit Techniken des Schliessens unter Unsicherheit, des maschinellen Lernens und dem Sprachverstehen. 
Der Kurs baut auf der Vorlesung Künstliche Intelligenz I vom Wintersemester auf und führt diese weiter. 

Lernziele und Kompetenzen
Fach- Lern- bzw. Methodenkompetenz

  • Wissen: Die Studierenden lernen grundlegende Repräsentationsformalismen und Algorithmen der Künstlichen Intelligenz kennen.

  • Anwenden: Die Konzepte werden an Beispielen aus der realen Welt angewandt (bungsaufgaben).

  • Analyse: Die Studierenden lernen über die Modellierung in der Maschine menschliche Intelligenzleistungen besser einzuschätzen.

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