Understanding the actual abstract reasons for agents' decisions (such as decisions about agreeing with a given argument) seems to me a promising idea, I'm trying to make progress on that (agents' decisions don't need to be correct or well-defined on most inputs for the reasons behind their more well-defined behaviors to lead the way to figuring out what to do in other situations or what should be done where the agents err). Note that if you postulate an algorithm that makes use of humans as its elements, you'd still have the problems of failure modes, regret for bad design decisions and of the capability to answer humanly incomprehensible questions, and these problems need to be already solved before you start the thing up.
Interesting, if I understand correctly the idea is to find a theoretically correct basis for deciding on a course of action given existing knowledge and then to make this calculation efficient and then direct towards a formally defined objective.
As distinct from a system which potentially sub optimally, attempts solutions and tries to learn improved strategies. i.e. one in which the theoretical basis for decision making is ultimately discovered by the agent over time (e.g. as we have done with the development of probability theory). I think the perspective...
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.]