So it's not all bad.

The problem that I want to talk with you about is really the problem of

I gave you an example of perception using particle filters.

We need to deliver health care, not in large institutions, but close to people's homes, and we need to deliver health care using whoever is available and affordable in our local communities.

I don't like being patient when systems are broken, and health care is broken.

The financial drain on our healthcare industry is immeasurable.

This is a beautiful framework.

Notice that here we're introducing a new variable.

We never did that before in planning.

So today is an exciting day.

Disorder is feared...

So the only way to do this is to watch videos.

Resources are things like this pile of nuts and bolts that are used somewhere in a plan.

So the moral is we need some feedback from the environment. We can't just plan ahead and come up with a whole plan.

Thrun So let me give you some examples of this method in action.

We defined AI to be the study and process of finding appropriate actions for an agent.

Narrator Planning under uncertainty.

So here is the situation that does work and it's related to information space or belief space.

That is, taking the action of perceiving something.

So each state, except for the absorbing states, we have to define an action to define a policy.

It goes south exclusively to gather information.

Here's one more topic.

And we're not supposed to be unreasonable in health, and so I hate this word patient.

So by doing both, and by observing that you don't consume the bandwidth, it still stays with you, you can watch them.

Of course, resources could be handled just in the language of classical planning.

You can't express the notion of All in propositional languages like classical planning, but you can in first order logic.

But you'll find a single sequence of actions and states that might cut the corners, as close as possible, to reach the plus 100 as fast as possible.

We talked a little bit about machine learning, a lot about particle filters, and some about motion planning, which relates to the planning class that Peter taught you quite a while back.

One of the key problems is that these robots have to decide what to do next.

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

Whereas if you need memory, it's partially observable.

Narrator We've been considering sensor less planning in a deterministic world.

The second is basically an entrepreneur who is set up and doing who knows what in a table in a market.

We distinguished deterministic was the casting environments, and we also talked about photos as partial observable.

And the way I think about this is that healthcare is like any other system.

So we made a robot that went inside and built maps of those mines.

Now we have to figure out what actions look like and what the results of those actions look like.

And I really am troubled that if Obama were to lose, we're gonna go backwards, and I don't think health care will ever be taken on again because of the political dangers of it.

Now, the trickiest part about situation calculus is describing what changes and what doesn't change as a result of an action.

And so is it any wonder, given all of the costs and the side effects of the drugs that we're using to treat these preconditions, that every year we're spending more than two trillion dollars on healthcare and yet 100,000 people a year and that's a conservative estimate are dying not because of the conditions they have, but because of the treatments that we're giving them and the complications of those treatments?

This unit we'll return to the topic of planning, and we'll talk about 4 things that we left out last time we talked about planning.

lot of the predecessors here. What are some of the little geeky easter eggs that I might not have noticed? Well there's one that's sort of a spoiler alert during the credits so we won't go to that.

It became quickly apparent to me that I couldn't follow the sorts of mental health care models that I had been trained in, one that relied heavily on specialized, expensive mental health professionals to provide mental health care in countries like India and Zimbabwe.

The first is that we need to simplify the message that we're using, stripping away all the jargon that medicine has invented around itself.

So what you'll find in the last case, E, is that the dollar amount and the bandwidth is insufficient to make you knowledgeable about planning and scheduling at the same time.