We're going to learn the basics of probability theory, and I'm assuming that's a recap,

but I'm going to recap it anyway because I want to be sure that we all use the same notation.

And then we're going to move up to Bayesian networks as compact and efficient ways of

doing inference with probabilities in an uncertain way.

So in a way, you should think about these kind of decision theoretic agents as agents

who use a Bayesian network representation of how the relevant aspects of the world work.

And that's what we're going to build up to.

And then we're going to graft on decision theory to that and going from Bayesian networks

to decision theoretic networks.

It's just going to be very easy.