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by Paul Christiano 549 days ago | Jessica Taylor likes this | link | parent

Having separate models \(P\) and \(Q\) is already quite weird; usually there would be a single model where values appear as latent structure.

You could legitimately complain that it seems very hard to construct such a model. And indeed I am skeptical that it will be possible. But if you want to fix problems arising from specifying \(Q\) rather than \(P\), it seems like you should say something about why specifying a separate \(Q\) is easier, or why someone would do it. At face value it looks equally difficult.

(Also, it is definitely not clear what algorithm you are referring to in this comment. Can you specify what computation the AI actually does / what kind of objects this \(P\) and \(Q\) are? The way I can see to make it work, \(P\) is a distribution over observations and \(Q\) is a distribution over values conditioned on observations. Is that right?)

by Stuart Armstrong 548 days ago | link

The model \(P\) is simply a model of human behaviour. It’s objective in the sense that it simply attempts to predict what humans will do in practice. It is, however, useless for figuring out what human values are, as it’s purely predictive of observations.

The model \(Q\) is an explanation/model for deducing human preferences or values, from observations (or predicted observations). Thus, given \(P\) and \(Q\), you can construct \(R\), the human reward function (note that \(P\), \(Q\), and \(R\) are all very different types of objects).

Simple possible \(Q\)’s would be \(Q_1\) = “everything the human does is rational” or \(Q_2\) = “everything the human does is random”.

So each \(Q\) contains estimates of rationality, noise, bias, amount of knowledge, and so on. Generally you’d want to have multiple \(Q\)’s and update them in terms of observations as well.


by Paul Christiano 548 days ago | link

What kind of object is \(Q\)? (I assume its not a string.) Are you directly specifying a distribution of preferences conditioned on observations? Are you specifying a distribution over observations conditioned on preferences and then using inference?

I assume the second case. So given that \(Q\) is a predictive model, why wouldn’t you also use \(Q\) as your model for planning? What is the advantage of using two separate models? Has anyone proposed using separate models in this way?

To the extent that your model \(Q\) is bad, it seems like you are just doomed to perform badly, and the you either need to abandon the model-based approach or come up with a better model. Adding a second model \(P\) doesn’t sound promising at face value.

It may be interesting or useful to have two models in this way, but I think it’s an unusual architecture that requires some discussion.






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