A second terminology for environments pertains to whether the environment is deterministic or stochastic.

Now, in this version, there are no sensors and so no percepts.

Here's a game that we've seen before.

I took a logical point of view.

The first question is from Incu in New York.

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

You really don't want to wait for the truck to disappear.

There must be a finite number of actions to chose from.

Finally.

Here is an MDP question.

And so we end up with a larger belief state here.

I wish to know whether it is stochastic.

You accumulate knowledge throughout your life in memories.

Games with dice, for example, like backgammon, are stochastic.

Critical.

Now what is the act observe cycle do to the sizes of the belief states?

But let's think about this. How can you make a small number of neurons do a lot?

The world will run smoother when we integrate and admit that.

So, suddenly it becomes perfectly rational to share needles.

We have to find a conclusive test.

You might remember in the very first class we distinguished very different characteristics of agent tasks, and here are some of those.

I look at it from an aesthetic point of view.

For that we have to ask, what is the nature of mind?

Yes, statistical... So he...

Random processes are said to be nondeterministic, since they're impossible to determine in advance.

So here are the 4 concepts again partially observable versus fully, stochastic versus deterministic, continuous versus discrete, adversarial versus benign.

In the core classical planning, the belief state had to be a complete assignment, and that was useful for dealing with deterministic fully observable domains.

The evidence is conclusive.

They want to spend less and save more.

Seems the logical assumption.

We've certainly used logic all over the place in this class.

The judge reasoned it out.

Watch the hallway. What I did get isn't much.

(Applause)

Yes from a managerial standpoint.

George asked, While it's possible to constantly re plan if enough computational power is available,

The MDP algorithms which I will talk about first fall into the intersection of fully observable yet stochastic, and just to remind us what the difference was, stochastic is an environment where the outcome of an action is somewhat random.

The purpose of a conclusion is to sum up the main points of the essay.

This decision is final.

But it doesn't.

I am connected to everything and everyone. I was born like that.

It was big news, but this... this is better.

Take the action of going north from the from the cell over here.

So, we have 97, 94, 91, 88, 85 and so on.

So suppose we had a grid cell, and we attempt to go north.