5 ms·

The Frame Problem (2016)

38 points by benbreen 9y ago

erikpukinskis 9y ago
Every time I see someone make an illogical prediction about AI, it's because they don't understand the frame problem. Immense processing power is of no value, if you have no capability of grounding and regrounding it adaptively in reality. That's the job our bodies and our societies provide, and AIs have no way to do it, except taking on a human body and joining human society, at which point an argument can be made they're not AIs any more, but human cyborgs.
- yorwba 9y ago
  While I agree that AIs need lots of experience with all kinds of situations to take all of them into account correctly, I don't think that a human body is necessary for that. It could be a network of surveillance cameras, or a fleet of sensor-stuffed cars or any other source of large numbers of observations. To predict the effect of an action, you don't need to be able to execute it yourself; or even if you can do it, there is no need to have done it; simply observing it is enough. There is no reason to suggest that AIs couldn't learn similarly through observation.
  - erikpukinskis 9y ago
    I don't mean to be rude, but did you read the article? The reason AIs can't learn just from observation is the frame problem. You use your body to interpret what you see. Animals who are paralyzed from birth are blind, not because they have no eyes, but because they have no embodied experiences to allow them to interpret what they are seeing.
    - yorwba 9y ago
      In spite of these subtleties, a number of solutions to the technical frame problem now exist that are adequate for logic-based AI research. Although improvements and extensions continue to be found, it is fair to say that the dust has settled, and that the frame problem, in its technical guise, is more-or-less solved (Shanahan 1997; Lifschitz 2015). Yes, I read the article. > Animals who are paralyzed from birth are blind, not because they have no eyes, but because they have no embodied experiences to allow them to interpret what they are seeing. That's interesting. Where can I learn more about that?
      - erikpukinskis 9y ago
        https://io9.gizmodo.com/the-seriously-creepy-two-kitten-experiment-1442107174 https://io9.gizmodo.com/the-seriously-creepy-two-kitten-expe...
phkahler 9y ago
This seems really stupid to me. An action has defined effects, there is no reason to think of all possible nonexistent side effects. Then they allow for that as a default and point out an apparent exception: what if an object is moved into a can of paint... This is so incredibly stupid I can't imagine people take this seriously.
- danharaj 9y ago
  The first interpretation of the frame problem is about mathematical entailment. If it isn't stated that a particular predicate on situations is preserved by taking actions, then it does not follow that the state is preserved. If you want to reason about situations in first order logic you need to pin the given theory down so that non-effects become deducible from the axioms. Either you can introduce frame axioms, which are tedious and slow, or you can try to solve the problem in a more sophisticated way. It's definitely not a trivial problem. A problem about logic in the narrow technical sense often has analogies in less technical philosophical considerations. That's what the rest of the article is about. You're taking too much for granted about how to formalize reasoning to much how humans intuitively go about it.
- lisper 9y ago
  > there is no reason to think of all possible nonexistent side effects But there is. Actually there are two reasons. The first is that the "non-existent side-effects" are only non-existent in certain circumstances, i.e. an action's effects depends heavily on the context in which it is executed. Moving X from A to B can have lots of other effects beyond changing the location of X depending on the circumstances. These effects can be arbitrary -- consider for example the case where X is a switch and A is the OFF position and B is the ON position. The second is that there really is no such thing as a truly non-existent side-effect because even a null action -- waiting, doing nothing -- results in change, and sometimes that change turns out to be the best way to achieve your goal. For example, if you're a farmer whose goal is to water their crops, one way to do it is to do nothing and wait for rain. But to figure out whether or not that's what you should do you have to know an awful lot about how the world works. In fact, waiting and doing nothing is often an integral part of many goal-achieving procedures. Think about, for example, traveling as a passenger on an airplane. The usual procedure goes something like: Buy a ticket Wait Go to the airport Wait Board the plane Wait Disembark the plane If you leave out the null actions, that procedure fails badly.
deleted 9y ago
[deleted]
ianbicking 9y ago
I was playing recently with logical planning [1], and heuristics to determine what possible actions to explore. A heuristic that I happened upon, that worked quite well, was to work backward from the desired solution and choose actions that reduced the requirements on the state of the world. The result was not plans that were optimal, but it found plans quite quickly. When I changed it to find optimal plans (essentially performing a reverse breadth-first search) then it did so, but instead of "finding" a solution it simply exhausted the space of actions and states. The algorithm stopped seeming intelligent, and merely felt thorough. I wonder if this is state-requirement-reducing heuristic is closer to the problem-solving approach we use. Not an approach where we understand the world thoroughly, but one where we make plans that require the least specificity of the state of the world. In part this is because our planning is iterative; given a goal: plan towards the goal, perform an action, observe the state, plan towards the goal, perform the next action. Unexpected effects are accounted for... eventually. And the result is often not optimal. Also logical planning does not introduce conditionals, even though our plans always contain conditionals. To use the Yale Shooting Problem [2], we might construct a higher-order action, like "shoot the turkey" – and implicit in this action is "if the gun is not loaded, load the gun, then shoot the turkey". Also "if the turkey has escaped, find the turkey, then shoot the turkey". If the turkey escapes while you are loading the gun, then perhaps a better plan would have included securing the turkey before loading the gun, but this is exactly the sort of mistake that humans make in their planning. With conditionals it's even easier to have actions that reduce the specificity of the requirements to achieve a goal. But the plans will probably not be optimal, but still they are resilient to a world with unintended consequences. [1] https://github.com/ianb/bitplanning https://github.com/ianb/bitplanning [2] https://en.wikipedia.org/wiki/Yale_shooting_problem https://en.wikipedia.org/wiki/Yale_shooting_problem
2sk21 9y ago
I'm just happy that somebody thought it worthwhile to post this article here. With all of the heard running towards ML, its great to see that not everyone has forgotten GOFAI inspired issues!

38 points by benbreen 9y ago

Every time I see someone make an illogical prediction about AI, it's because they don't understand the frame problem. Immense processing power is of no value, if you have no capability of grounding and regrounding it adaptively in reality. That's the job our bodies and our societies provide, and AIs have no way to do it, except taking on a human body and joining human society, at which point an argument can be made they're not AIs any more, but human cyborgs.

yorwba 9y ago
While I agree that AIs need lots of experience with all kinds of situations to take all of them into account correctly, I don't think that a human body is necessary for that. It could be a network of surveillance cameras, or a fleet of sensor-stuffed cars or any other source of large numbers of observations. To predict the effect of an action, you don't need to be able to execute it yourself; or even if you can do it, there is no need to have done it; simply observing it is enough. There is no reason to suggest that AIs couldn't learn similarly through observation.
- erikpukinskis 9y ago
  I don't mean to be rude, but did you read the article? The reason AIs can't learn just from observation is the frame problem. You use your body to interpret what you see. Animals who are paralyzed from birth are blind, not because they have no eyes, but because they have no embodied experiences to allow them to interpret what they are seeing.
  - yorwba 9y ago
    In spite of these subtleties, a number of solutions to the technical frame problem now exist that are adequate for logic-based AI research. Although improvements and extensions continue to be found, it is fair to say that the dust has settled, and that the frame problem, in its technical guise, is more-or-less solved (Shanahan 1997; Lifschitz 2015). Yes, I read the article. > Animals who are paralyzed from birth are blind, not because they have no eyes, but because they have no embodied experiences to allow them to interpret what they are seeing. That's interesting. Where can I learn more about that?
    - erikpukinskis 9y ago
      https://io9.gizmodo.com/the-seriously-creepy-two-kitten-experiment-1442107174 https://io9.gizmodo.com/the-seriously-creepy-two-kitten-expe...

phkahler 9y ago

This seems really stupid to me. An action has defined effects, there is no reason to think of all possible nonexistent side effects. Then they allow for that as a default and point out an apparent exception: what if an object is moved into a can of paint... This is so incredibly stupid I can't imagine people take this seriously.

danharaj 9y ago
The first interpretation of the frame problem is about mathematical entailment. If it isn't stated that a particular predicate on situations is preserved by taking actions, then it does not follow that the state is preserved. If you want to reason about situations in first order logic you need to pin the given theory down so that non-effects become deducible from the axioms. Either you can introduce frame axioms, which are tedious and slow, or you can try to solve the problem in a more sophisticated way. It's definitely not a trivial problem. A problem about logic in the narrow technical sense often has analogies in less technical philosophical considerations. That's what the rest of the article is about. You're taking too much for granted about how to formalize reasoning to much how humans intuitively go about it.
lisper 9y ago
> there is no reason to think of all possible nonexistent side effects But there is. Actually there are two reasons. The first is that the "non-existent side-effects" are only non-existent in certain circumstances, i.e. an action's effects depends heavily on the context in which it is executed. Moving X from A to B can have lots of other effects beyond changing the location of X depending on the circumstances. These effects can be arbitrary -- consider for example the case where X is a switch and A is the OFF position and B is the ON position. The second is that there really is no such thing as a truly non-existent side-effect because even a null action -- waiting, doing nothing -- results in change, and sometimes that change turns out to be the best way to achieve your goal. For example, if you're a farmer whose goal is to water their crops, one way to do it is to do nothing and wait for rain. But to figure out whether or not that's what you should do you have to know an awful lot about how the world works. In fact, waiting and doing nothing is often an integral part of many goal-achieving procedures. Think about, for example, traveling as a passenger on an airplane. The usual procedure goes something like: Buy a ticket Wait Go to the airport Wait Board the plane Wait Disembark the plane If you leave out the null actions, that procedure fails badly.

deleted 9y ago

[deleted]

ianbicking 9y ago

I was playing recently with logical planning [1], and heuristics to determine what possible actions to explore. A heuristic that I happened upon, that worked quite well, was to work backward from the desired solution and choose actions that reduced the requirements on the state of the world. The result was not plans that were optimal, but it found plans quite quickly. When I changed it to find optimal plans (essentially performing a reverse breadth-first search) then it did so, but instead of "finding" a solution it simply exhausted the space of actions and states. The algorithm stopped seeming intelligent, and merely felt thorough. I wonder if this is state-requirement-reducing heuristic is closer to the problem-solving approach we use. Not an approach where we understand the world thoroughly, but one where we make plans that require the least specificity of the state of the world. In part this is because our planning is iterative; given a goal: plan towards the goal, perform an action, observe the state, plan towards the goal, perform the next action. Unexpected effects are accounted for... eventually. And the result is often not optimal. Also logical planning does not introduce conditionals, even though our plans always contain conditionals. To use the Yale Shooting Problem [2], we might construct a higher-order action, like "shoot the turkey" – and implicit in this action is "if the gun is not loaded, load the gun, then shoot the turkey". Also "if the turkey has escaped, find the turkey, then shoot the turkey". If the turkey escapes while you are loading the gun, then perhaps a better plan would have included securing the turkey before loading the gun, but this is exactly the sort of mistake that humans make in their planning. With conditionals it's even easier to have actions that reduce the specificity of the requirements to achieve a goal. But the plans will probably not be optimal, but still they are resilient to a world with unintended consequences. [1] https://github.com/ianb/bitplanning https://github.com/ianb/bitplanning [2] https://en.wikipedia.org/wiki/Yale_shooting_problem https://en.wikipedia.org/wiki/Yale_shooting_problem

2sk21 9y ago

I'm just happy that somebody thought it worthwhile to post this article here. With all of the heard running towards ML, its great to see that not everyone has forgotten GOFAI inspired issues!