The budget appears to be inaccurate and unrealistic.

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

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.

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.

Yeah...

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

Zero to 10. How high? 10.

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

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

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.

Probabilities are the cornerstone of artificial intelligence.

Kind of exciting.

We have the same needs.

It's wrong. It's 0. It should be 5, but it's 0.

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

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

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

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

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.

So today is an exciting day.

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

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

First, it can only handle true and false values.

However, I have on good authority that that many of the largest websites from the world actually do things that way because testing website is fairly non deterministic.

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

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

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

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.

The environment is clearly stochastic.

When you run this, your first estimate for position should basically become 5 4.99, and the reason is your initial uncertainty is so large, the estimate is dominated by the first measurement.

If we plug in the nth measurement and the measurement uncertainty, it does the same with the motion, the prediction part over here.

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

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

like 23 million.

The answer is yes.

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

That gives me my new x.

The next one comes from MJL again.

Physically based rendering is also a great help to virtual world content creators, such as game and film makers.

It's 4.98.

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

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

Here is a quick quiz.