So, N of rational agents, we're looking at a certain class of algorithms that can work

with atomic state representations. And the main thing we looked at was kind of the prerequisites

for running these algorithms. And the prerequisite is that we can describe the world or the environment

atomically, which means we can write down a couple of states, states we do not have

to look into, and a set of actions or a relation, which is the successive relation, mathematically

the same thing. The whole setup will be that if you can do this, if you can formulate your

problem using atomic states and actions, and little twiddly bits, which we looked at, then

you can run the search algorithms we're going to look at in a little bit more detail. And

really the search algorithms are relatively simple, and you've seen them before, I will

just briefly go through them, assuming that you did. The real step in this is somebody,

usually a human agent designer, actually sits down and looks at the problem and encodes

it in a state action for a relation. And then you take the algorithm from the shelf, bang

hard on it, and if you're lucky, something good comes out. An example we'll be looking

at quite often, because it's so nice, is pathfinding navigation in Romania. Essentially you want

to go to Bucharest, you are in Arad, and a good way of representing the states is just

equating them with, I am in city, say Arad, and you have the actions you take, which is

something like, go to CBU. Sometimes we have cost functions with the actions, and the obvious

solution to this is that you have a sequence of states that will end in a goal state. Okay?

And it is very easy. We have written it down in maths, which is good for understanding

exactly what we're talking about. And that allows us to reason about these things.