Are there any parts of theoretical computer science that you expect will be useful?
If you think the subject matter of mathematical logic will be useful then it might be worthwhile to take a look at the Curry-Howard correspondence. From the link:
In other words, the Curry–Howard correspondence is the simple observation that two at-the-time-seemingly-unrelated families of formalisms, the proof systems on one side and the models of computation on the other side, were, on the two examples considered by Curry and Howard, in fact structurally the same kind of objects.
This post enumerates texts that I consider (potentially) useful training for making progress on Friendly AI/decision theory/metaethics.
Rationality and Friendly AI
Eliezer Yudkowsky's sequences and this blog can provide solid introduction to the problem statement of Friendly AI, giving concepts useful for understanding motivation for the problem, and disarming endless failure modes that people often fall into when trying to consider the problem.
For a shorter introduction, see
Decision theory
The following book introduces an approach to decision theory that seems to be closer to what's needed for FAI than the traditional treatments in philosophy or game theory:
Another (more technical) treatment of decision theory from the same cluster of ideas:
Following posts on Less Wrong present ideas relevant to this development of decision theory:
Mathematics
The most relevant tool for thinking about FAI seems to be mathematics, where it teaches to work with precise ideas (in particular, mathematical logic). Starting from a rusty technical background, the following reading list is one way to start:
[Edit Nov 2011: I no longer endorse scope/emphasis, gaps between entries, and some specific entries on this list.]