Ok, so how about this work around.
The current approach is to have a number of human intelligences continue to explore this problem until they enter a mental state C (for convinced they have the answer to FAI). The next stage is to implement it.
We have no other route to knowledge other than to use our internal sense of being convinced. I.e. no oracle to tell us if we are right or not.
So what if we formally define what this mental state C consists of and then construct a GAI which provably pursues only the objective of creating this state. The advantage being that we now have a means of judging our progress because we have a formally defined measurable criteria for success. (In fact this process is a valuable goal regardless of the use of AI but it now makes it possible to use AI techniques to solve it).
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.]