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

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.

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

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

We have the same needs.

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

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

Zero to 10. How high? 10.

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

Kind of exciting.

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...

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

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.

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

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

And what we add is the constraint itself, but it's up multiplied by a strength factor.

It's 4.98.

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

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

I will find myself in the same state as before.

And fifth, the element of uncertainty.

That gives me my new x.

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

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

But they do the job.

The next one comes from MJL again.

Because what I've been trying to do is really celebrate uncertainty.

You loosen your hold on heaven only to give rein to the whim of these sinful twins the fruit of your infidelity

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

So today is an exciting day.

The observation partitions the belief state into smaller belief states.

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

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

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

And now when the next measurement comes in, 6, we are now more inclined to believe the measurement because uncertainty is now basically 2 as opposed to 0.001.

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.

At this point unconfirmed reports Indicate that

That makes 2σ² the correct answer.

Arlene told me people are less calciumdeficient than they used to be because of all the fancy coffee they drink nowadays.

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

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.

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.

First, it can only handle true and false values.