|
|
|
|
|
by eli_gottlieb
4091 days ago
|
|
>Animals in general and humans in particular have a large number of conflicting drives, which interact in complicated ways. They are also thrust into environments which have complicated dynamics and where the overall state (i.e., all relevant information) is not necessarily available. Yes, but the actual mechanism by which the animal learns what to do, as it turns out, thanks theoretical neuroscience, is basically reinforcement learning. So it is very likely that the first powerful artificial agents will be reinforcement learners, because scientists usually prototype and experiment by duplicating from Nature. And nothing in reinforcement learning particularly stops the agent from just grabbing its electronic crack-pipe and doing its own thing. |
|
Another argument might be that nothing stops you or I from electing to abandon everything for the nearest crack den, either... except for the fact that we have learned, from interacting with our environment, that there are other things we enjoy, and that cocaine addiction might be more destructive than desirable over the timescale we're interested in.
Supposing we have an agent that wants to create a lot of paperclips, it might avoid reaching for the crack-pipe of terraforming Singapore because it realizes that would delay the shipments of raw materials it needs for its factories elsewhere in the world. If the agent's goals are more complicated than that, we might expect increasingly complicated behaviors, just like how humans operating on fairly simple drives/reward functions have erected a few more tiers above the primitive needs in Maslow's hierarchy.
---
1. Off the top of my head, the abstracts on pages 37 & 193 seem to be relevant. http://www.princeton.edu/~yael/RLDM2013ExtendedAbstracts.pdf