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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Dauer
01:27:28 Min
Aufnahmedatum
2018-05-16
Hochgeladen am
2018-05-17 11:31:43
Sprache
en-US
Der Kurs baut auf der Vorlesung Künstliche Intelligenz I vom Wintersemester auf und führt diese weiter.
Lernziele und Kompetenzen
Fach- Lern- bzw. Methodenkompetenz
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Wissen: Die Studierenden lernen grundlegende Repräsentationsformalismen und Algorithmen der Künstlichen Intelligenz kennen.
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Anwenden: Die Konzepte werden an Beispielen aus der realen Welt angewandt (bungsaufgaben).
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Analyse: Die Studierenden lernen über die Modellierung in der Maschine menschliche Intelligenzleistungen besser einzuschätzen.