OK, let's start.

So as an orientation, we're in the midst

of looking at Bayesian networks.

Bayesian networks as a world model that

allows probabilistic inference.

So the idea is that an agent has to model the world somehow.

And in an uncertain world, we have

to do something about the fact that we can't see everything,

that our actions might be unsure to succeed,

that we have unreliable sensors, and all of those kind of things.

In all of those cases, we have to deal with possible worlds

and our estimations of likelihood of those worlds

and still make good decisions.

I think we're going to look into decision theory next.

So far, we've only done modeling, setting up models.

And in particular, yesterday we talked

about constructing Bayesian networks rationally

and then computing probabilities based on that.

Just basically, given some evidence,

how probable is it that it's going to rain tomorrow

or something like this?

Well, actually, we haven't done time yet.

That'll come as well.

What is the probability of that is raining outside right now?

We had some evidence walking here.

It didn't, and there weren't any clouds,

so those kind of things.

And Bayesian networks is our tool of choice

because it gives us a good way of representing and computing

with conditional independence.

Conditional independence as our prime lifesaver

in terms of computational complexity.

And of course, computational complexity

has its cost for an agent.

So we have this basic Bayesian network construction algorithm

that just basically said, take your variables,

order them in some way, and then just

compute the conditional probability tables.

And from the conditional probability tables,

you can see what the dependencies are

by the test of just dropping a variable from the givens

and see whether something changes.

And then we have a model.

Because if there is a dependency,

we just add it to the graph, add the CPT to the graph,

and then we end up with something.

And depending on the order we looked at the example,

we get less pretty graphs where pretty, less pretty

means more arrows than in other ways.

And whenever you think it gets bad,