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
- Eliezer S. Yudkowsky (2008). "Artificial Intelligence as a Positive and Negative Factor in Global Risk". Global Catastrophic Risks. Oxford University Press.
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:
- G. L. Drescher (2006). Good and Real: Demystifying Paradoxes from Physics to Ethics (Bradford Books). The MIT Press, 1 edn.
Another (more technical) treatment of decision theory from the same cluster of ideas:
- E. Yudkowsky. Timeless Decision Theory (draft, Sep 2010)
Following posts on Less Wrong present ideas relevant to this development of decision theory:
- A Priori
- Newcomb's Problem and Regret of Rationality
- The True Prisoner's Dilemma
- Counterfactual Mugging
- Timeless Decision Theory: Problems I Can't Solve
- Towards a New Decision Theory
- Ingredients of Timeless Decision Theory
- Decision theory: Why Pearl helps reduce "could" and "would", but still leaves us with at least three alternatives
- The Absent-Minded Driver
- AI cooperation in practice
- What a reduction of "could" could look like
- Controlling Constant Programs
- Notion of Preference in Ambient Control
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.]
- F. W. Lawvere & S. H. Schanuel (1991). Conceptual mathematics: a first introduction to categories. Buffalo Workshop Press, Buffalo, NY, USA.
- B. Mendelson (1962). Introduction to Topology. College Mathematics. Allyn & Bacon Inc., Boston.
- P. R. Halmos (1960). Naive Set Theory. Springer, first edn.
- H. B. Enderton (2001). A Mathematical Introduction to Logic. Academic Press, second edn.
- S. Mac Lane & G. Birkhoff (1999). Algebra. American Mathematical Society, 3 edn.
- F. W. Lawvere & R. Rosebrugh (2003). Sets for Mathematics. Cambridge University Press.
- J. R. Munkres (2000). Topology. Prentice Hall, second edn.
- S. Awodey (2006). Category Theory. Oxford Logic Guides. Oxford University Press, USA.
- K. Kunen (1999). Set Theory: An Introduction To Independence Proofs, vol. 102 of Studies in Logic and the Foundations of Mathematics. Elsevier Science, Amsterdam.
- P. G. Hinman (2005). Fundamentals of Mathematical Logic. A K Peters Ltd.
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... (read more)