I think it depends on how you define expected utility. I agree that a definition that limits us only to analyzing end-state maximizers that seek some final state of the world is not very useful.
I don’t think that for non-trivial AI agents, the utility function should or even can be defined as a simple function over the preferable final state of the world. U:Ω→R
This function does not take into account time and an intermediate set of predicted future states that the agent will possibly have preference over. The agent may have a preference for the final state of the universe but most likely and realistically it won’t have that kind of preference except for some special strange cases. There are two reasons:
a general agent likely won’t be designed as a maximizer over one single long-term goal (like making paperclips) but rather as useful for humans over multiple domains so it would rather care more about short-term outcomes, middle-term preferences, and tasks “at hand”
the final state of the universe is generally known by us and will likely be known by a very intelligent general agent, even if you ask current GPT-3 it knows that we will end up in Big Freeze or Big Rip with the latter being more likely. Agent can’t really optimize for the end state of the Universe as there are not many actions that could change physics and there is no way to reason about the end state except for general predictions that do not end up well for this universe, whatever the agent does.
Any complex agent would likely have a utility function over possible actions that would be equal to the utility function of the set of predicted futures after action A vs the set of predicted futures without action A (or over differences between worlds in those futures). By action I mean possibly a set of smaller actions (hierarchy of actions—e.g. plans, strategies), it might not be atomic. Directly it cannot be easily computable so most likely this would be compressed to a set of important predicted future events on the level of abstraction that the agent cares about, which should constitute future worlds without action A and action A with enough approximation.
This is also how we evaluate actions. We evaluate outcomes in the short and long terms. We also care differently depending on time scope.
I say this because most sensible “alignment goals” like please don’t kill humans are time-based. What does it mean not to kill humans? It is clearly not about the final state. Remember, Big Rip or Big Freeze. Maybe AGI can kill some for a year and then no more assuming the population will go up and some people are killed anyway so it does not matter long-term? No, this is also not about the non-final but long-term outcome. Really it is a function of intermediate states. Something like the integral of some function U’(dΩ) where dΩ is a delta between outcomes of action vs non-action, over time, which can be approximated and compressed into integral over the function of an event over multiple events until some time T being maximal sensible scope.
Most of the behaviors and preferences of humans are also time-scoped, and time-limited and take multiple future states into account, mostly short-scoped. I don’t think that alignment goals can be even expressed in terms of simple end-goal (preferable final state of the world) as the problem partially comes from the attitude of eng goal justifying the means that are at the core of the utility function defined as U:Ω→R.
It seems plausible to me that even non-static human goals can be defined as utility functions over the set of differences in future outcomes (difference between two paths of events). What is also obvious to me is that we as humans are able to modify our utility function to some extent, but not very much. Nevertheless, for humans the boundaries between most baseline goals, preferences, and morality vs instrumental convergence goals are blurry. We have a lot of heuristics and biases so our minds work out some things more quickly and more efficiently than if we would on intelligence, thinking, and logic. The cost is lower consistency, less precision, and higher variability.
So I find it useful to think about agents as maximizers over utility function, but not defined as one final goal or outcome or state of the world. Rather one that maximizes the difference between two ordered sets of events in different time scopes to calculate the utility of an action.
I also don’t think agents must be initially rationally stable with an unchangeable utility function. This is also a problem as an agent can have initially a set of preferences with some hierarchy or weights, but it also can reason that some of these are incompatible with others, that the hierarchy is not logically consistent, and might seek to change it for sake of consistency to be fully coherent.
I’m not an AGI, clearly, but it is just like I think about morality right now. I learned that killing is bad. But I still can question “why we don’t kill?” and modify my worldview based on the answer (or maybe specify it in more detail in this matter). And it is a useful question as it says a lot about edge cases including abortion, euthanasia, war, etc. The same might happen for rational agents—as it might update their utility function to be stable and consistent, maybe even questioning some of the learned parts of the utility function in the process. Yes, you can say that if you can change that then it was not your terminal goal. Nevertheless, I can imagine agents with no terminal core goals at all. I’m not even sure if we as humans have any core terminal goals (maybe except avoiding death and own harm in the case of most humans in most circumstances… but some overcome that as Thích Quảng Đức did).
I think it depends on how you define expected utility. I agree that a definition that limits us only to analyzing end-state maximizers that seek some final state of the world is not very useful.
I don’t think that for non-trivial AI agents, the utility function should or even can be defined as a simple function over the preferable final state of the world. U:Ω→R
This function does not take into account time and an intermediate set of predicted future states that the agent will possibly have preference over. The agent may have a preference for the final state of the universe but most likely and realistically it won’t have that kind of preference except for some special strange cases. There are two reasons:
a general agent likely won’t be designed as a maximizer over one single long-term goal (like making paperclips) but rather as useful for humans over multiple domains so it would rather care more about short-term outcomes, middle-term preferences, and tasks “at hand”
the final state of the universe is generally known by us and will likely be known by a very intelligent general agent, even if you ask current GPT-3 it knows that we will end up in Big Freeze or Big Rip with the latter being more likely. Agent can’t really optimize for the end state of the Universe as there are not many actions that could change physics and there is no way to reason about the end state except for general predictions that do not end up well for this universe, whatever the agent does.
Any complex agent would likely have a utility function over possible actions that would be equal to the utility function of the set of predicted futures after action A vs the set of predicted futures without action A (or over differences between worlds in those futures). By action I mean possibly a set of smaller actions (hierarchy of actions—e.g. plans, strategies), it might not be atomic. Directly it cannot be easily computable so most likely this would be compressed to a set of important predicted future events on the level of abstraction that the agent cares about, which should constitute future worlds without action A and action A with enough approximation.
This is also how we evaluate actions. We evaluate outcomes in the short and long terms. We also care differently depending on time scope.
I say this because most sensible “alignment goals” like please don’t kill humans are time-based. What does it mean not to kill humans? It is clearly not about the final state. Remember, Big Rip or Big Freeze. Maybe AGI can kill some for a year and then no more assuming the population will go up and some people are killed anyway so it does not matter long-term? No, this is also not about the non-final but long-term outcome. Really it is a function of intermediate states. Something like the integral of some function U’(dΩ) where dΩ is a delta between outcomes of action vs non-action, over time, which can be approximated and compressed into integral over the function of an event over multiple events until some time T being maximal sensible scope.
Most of the behaviors and preferences of humans are also time-scoped, and time-limited and take multiple future states into account, mostly short-scoped. I don’t think that alignment goals can be even expressed in terms of simple end-goal (preferable final state of the world) as the problem partially comes from the attitude of eng goal justifying the means that are at the core of the utility function defined as U:Ω→R.
It seems plausible to me that even non-static human goals can be defined as utility functions over the set of differences in future outcomes (difference between two paths of events). What is also obvious to me is that we as humans are able to modify our utility function to some extent, but not very much. Nevertheless, for humans the boundaries between most baseline goals, preferences, and morality vs instrumental convergence goals are blurry. We have a lot of heuristics and biases so our minds work out some things more quickly and more efficiently than if we would on intelligence, thinking, and logic. The cost is lower consistency, less precision, and higher variability.
So I find it useful to think about agents as maximizers over utility function, but not defined as one final goal or outcome or state of the world. Rather one that maximizes the difference between two ordered sets of events in different time scopes to calculate the utility of an action.
I also don’t think agents must be initially rationally stable with an unchangeable utility function. This is also a problem as an agent can have initially a set of preferences with some hierarchy or weights, but it also can reason that some of these are incompatible with others, that the hierarchy is not logically consistent, and might seek to change it for sake of consistency to be fully coherent.
I’m not an AGI, clearly, but it is just like I think about morality right now. I learned that killing is bad. But I still can question “why we don’t kill?” and modify my worldview based on the answer (or maybe specify it in more detail in this matter). And it is a useful question as it says a lot about edge cases including abortion, euthanasia, war, etc. The same might happen for rational agents—as it might update their utility function to be stable and consistent, maybe even questioning some of the learned parts of the utility function in the process. Yes, you can say that if you can change that then it was not your terminal goal. Nevertheless, I can imagine agents with no terminal core goals at all. I’m not even sure if we as humans have any core terminal goals (maybe except avoiding death and own harm in the case of most humans in most circumstances… but some overcome that as Thích Quảng Đức did).