I'm trying to implement value change (see eg http://lesswrong.com/lw/jxa/proper_value_learning_through_indifference/ ). The change from u to -u is the easiest example of such a change. The ideal - which probably can't be implemented in a standard utility function - is that it is a u-maximiser that's indifferent to becoming a -u maximiser, who's then indifferent to further change, etc...
Well, then, let's change from the example being Monday + to Tuesday - to Wednesday and all later times +, with it unable to actually affect paperclip counts on Tuesday, let's consider if we just have a transition from u+ on Monday, Tuesday, Wednesday +, with u- on Thursday and later times, and it already has all the infrastructure it needs.
In this case, it will see that it can get a + score by having paperclips monday through wednesday, but that any that it still has on Thursday will count against it.
So, it will build paperclips as soon as it learns of thi...
A putative new idea for AI control; index here.
A simple and easy design for a u-maximising agent that turns into a u-minimising one.
Let X be some boolean random variable outside the agent's control, that will be determined at some future time t (based on a cosmic event, maybe?). Set it up so that P(X=1)=ε, and for a given utility u, consider the utility:
Before t, the expected value of (2/ε)X is 2, so u# = u. Hence the agent is a u-maximiser. After t, the most likely option is X=0, hence a little bit of evidence to that effect is enough to make u# into a u-minimiser.
This isn't perfect corrigibility - the agent would be willing to sacrifice a bit of u-value (before t) in order to maintain its flexibility after t. To combat this effect, we could instead use:
If Ω is large, then the agent is willing to pay very little u-value to maintain flexibility. However, the amount of evidence of X=0 that it needs to become a u-minimiser is equally proportional to Ω, so X better be a clear and convincing event.