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

It updates the mu and sigma using this recursive formula over here.

Isolate discontinuities and recalibrate.

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

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.

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

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

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.

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

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

For example, the doctor doesn't get to measure every symptom or every test result, and she's certainly uncertain about the diseases that the patient has.

Zero to 10. How high? 10.

Uncertainty comes because of noisy observation.

We then move to the right a total of 10, with a motion uncertainty of 6.

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

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

We have the same needs.

I gave you 4 key attributes of intelligent agents (partial observability, stochasticity, continuous spaces, and adversarial natures),

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.

The observation partitions the belief state into smaller belief states.

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

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.

Undetermined Links

Undetermined

We never brought planning and uncertainty together, uncertainty and learning, or planning and learning.

I want you to design an H matrix that's a projection matrix from 4 dimensional state space to 2 dimensions, reflecting the fact that we can only observe the first two state variables x and y but not the velocities.

First, it can only handle true and false values.

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

The environment is clearly stochastic.

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

There is no significant scientific dispute about that.

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.