Now, I would like to tell you that when we're designing agents, one of the central things,

one of the central choices we have to make is the language in which we describe the world.

Even more important than, oh no, I'm sorry.

One of the, got kind of surprised of what I was seeing here.

So one of the central things you can choose is the description language.

And you have choices there.

Some people want to tell you that there's only one logic.

And usually they say it's first order logic, which we didn't see here.

So that's wrong already.

We've seen propositional logic, which is good for certain things, which is very good because

we have decision procedures for satisfiability for it.

But really this is not a very nice language to talk about the world.

You see the Wampus Cave, say there, or this, and you want to know the rule.

You'd not probably be very happy if I said not S11 implies not W11 and not W12 and not

W21, and keep on for about three hours.

That's by the way not what I told you when we talked about Wampus the first time.

Well, I told you about things, well if there's a pit, then there's a breeze in the others,

and if there's a Wampus then it stinks in the adjacent things.

That's kind of a language you would like to use.

So we have a choice in which language we are.

We should choose a language for which we have efficient inference procedures, but there's

a kind of a big field of engineering.

And that has been a very active field of engineering in the last 60 years.

In particular, the first AI program ever in 54 was an inference procedure essentially

for a small superset called Pressberger arithmetic of propositional logic.

So this is an important thing.

Good.

So before we go into looking at better languages, and we're going to look at predicate logic,

we are going to look at state of the art satisfaction solvers for propositional languages.

And there's only really one game in town and that's called DPLL.

We looked at how this works.