Zero to 10. How high? 10.

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

At this point unconfirmed reports Indicate that

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

Of course, we can't know what will happen.

Every time the robot moves, it loses information as to where it is.

My English is incorrect.

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

First, it can only handle true and false values.

Kind of exciting.

We have the same needs.

So, what I'd like to finish with is this over here.

And fifth, the element of uncertainty.

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.

So today is an exciting day.

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

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

Yeah...

What the robot doesn't know it's starting in the middle of the corridor.

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

Or a pixel that might have one of several labels.

Probabilities are the cornerstone of artificial intelligence.

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

as always.

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.

But just as this state was uncertain not all the variables were known this state too will be uncertain.

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.

Last year was a period of economic uncertainty.

I will find myself in the same state as before.

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

It's very well documented that people get things wrong.

The environment is clearly stochastic.

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

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

So just to conclude what are the take home messages from this?

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

The budget appears to be inaccurate and unrealistic.

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.

I am convinced of his innocence.

There's the fuel you pick. So very quickly, six needs.

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

He understood a concept that is universal deep and associated with the equation, the concept of Entropy.

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

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.

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