And if you look at the end result, our estimate is almost exactly 11, which is the result of 10 1.

We have the same needs.

A gaussian with a small amount of uncertainty might look like this.

Then you arrive at a prediction that adds the motion of command to the mean, and it has an increased uncertainty over the initial uncertainty.

Yeah...

So it's 0, 0, 0, 0 please don't change it.

I will find myself in the same state as before.

I initialized both with 0 because I don't know the actual location and the velocity.

The velocity estimate is correctly 1, which is .99999, and I have yet another uncertainty matrix which illustrates

For my first question, I'll assume P equals 1.

Zero to 10. How high? 10.

This is 1.24 to the 10th, but a really small uncertainty, even smaller than this one over here.

We update our position to be 2.666, which is now a jump away from 1, and we reduce our uncertainty.

As a robot moves, its location uncertainty is going to increase.

And then the next update comes in at 6, and it gives us a measurement of 5.99 and now a reduced uncertainty of 2.39.

No neat packages of faith, with Bible references to prove them.

Probabilities are the cornerstone of artificial intelligence.

Kind of exciting.

First, it can only handle true and false values.

It's 4.98.

And then we go to move to the right again by 1, which makes the prediction 6.99.

So I love this picture, there is so much in it.

I learn nothing about the velocity, so it's still 0, just the way I initialized it.

We have a weighted mean over here.

We've surrendered our power, trading off our discomfort with uncertainty for the illusion of certainty that they provide.

And the way to see this is if we started over here at a well defined zero position, and you go to over here and you have some uncertainty as to where you might be.

You can actually derive what the centrifugal noise in the sensor and characterize those.

In this class so far we talked a good deal about planning.

This all would work out really well if the initial estimate was 5, but we're setting it to 0 with a very large uncertainty of 10,000.

So that was the word model . What about probabilistic ? The word

So today is an exciting day.

The answer is yes.

That gives me my new x.

You then predict that you add 1, but the uncertainty increases to 5.99, which is the motion uncertainty of 2.

We now apply the motion of 1. We get to 5.9.

It's moving to the right in this example, and it knows roughly how far it moved.

Suppose a robot drives down a corridor, and it senses surrounding walls.

Every time Google tweaks their algorithm, lots of people get upset because their PageRanks go down.

Or a pixel that might have one of several labels.

We talked about planning, but not under uncertainty, and you've had many, many classes of under uncertainty, and now it gets to the point where we can make decisions under uncertainty.

We've seen how a search can discover sequences of actions to solve problems.

And fifth, the element of uncertainty.

It might be possible to have a fairly small uncertainty in 1 dimension, but a huge uncertainty in the other.

At this point unconfirmed reports Indicate that

They got out on the far end, we said, Fine, don't worry about it.