Curiosity about Aligning Values

One interpretation of this would be imitation learning: teaching a system to imitate human strategies, rather than optimize some objective of its own.

The problem with imitation learning is: since humans are pretty smart, a close imitation of a human strategy is probably going to involve planning in the deliberate service of some values. So if you set a big neural network on the problem of imitating humans, it will develop its own preferences and ability to plan. This is a recipe for an inner optimizer. Its values and planning will have to line up with humans in typical cases, but in extreme cases (eg adversarial examples), it could be very different. This can be a big problem, because the existence of such an AI could itself push us to extreme cases where the AI has trouble generalizing.

Another interpretation of your idea could be "approval-directed agents". These are not trained to imitate humans, but rather, trained based on human approval of actions. However, unlike reinforcement learners, they don't plan ahead to maximize expected approval. They only learn to take specific actions more when they are approved of, and less when they earn disapproval.

Unlike imitation learners, approval-directed agents can be more capable than human trainers. However, unlike reinforcement learning agents, approval-directed agents don't have any incentive to take over control of their reward buttons. All the planning ahead comes from humans, looking at particular sorts of actions and deciding that they're good.

Unfortunately, this still faces basically the same problem as imitation learning. Because humans are approving/disapproving based on complicated models of the world and detailed thoughts about the consequences of actions, a big neural network has good reason to replicate those faculties within itself. You get an inner optimizer again, with the risks of misalignment that this brings.

Here's my question: what if instead of fixed concepts and rules, AI alignment focused on actions as the underlying reward function? In other words, might programming AI to focus on the means rather than the ends facilitate an environment in which humans are freer to act and reach their own ends, prioritizing activated potential over predetermined outcome? Can the action, instead of the outcome, become the parameter, rendering AI a facilitator rather than a determiner? 

If I'm understanding your right, which I'm not sure I am, I think this just collapses back to the normal case but where the thing being optimized for are those that you demarcate as "means" rather than "ends". That is, the means literally become the ends because they are the things being optimized for.

In principle, you could enumerate every possible scenario your AI system could encounter and specify what the best action is in that situation. In practice, this either requires an impossible amount of computer memory, an impossible knowledge of what the system is likely to encounter, an impossible amount of work to actually create and test the mapping of input states to output actions, or more commonly a combination of these.

Even if it could be done - in what sense could the result be meaningfully called "AI"? A modern computer behaves perfectly deterministically, always performing the same action under the same conditions, but they are tools, not intelligences, and they can't learn on their own or generalize to new inputs. An AI with an ability to understand natural language will eventually be able to learn and use words it hasn't heard before, but my computer will never "know" what to do if I remove the "enter" button from the keyboard and plug in a toaster.

I'm far from well-read on these topics myself so I'm likely misunderstanding the question or poorly answering it. I recommend looking at some of the curated sequences on AI safety on LessWrong (click the "Library" header in the sidebar menu and scroll for relevant titles). It's very possible your questions are addressed there.