Totally agree this is a useful distinction. The map/territory thing feels right on. This is something that the mainstream AI research community doesn’t seem confused about. As far as I can see, no one there thinks search and planning are the same task.
With regard to search algorithms being controllers: Here’s a discussion I had with ErickBall where they argue that planning will ultimately prove useful for search and I argue it won’t. There might also be some new ideas for “what’s the critical distinction” in that discussion.
I guess what I think isn’t that the mainstream isn’t explicitly confused about the distinction (ie, doesn’t make confused claims), but that it isn’t clearly made/taught, which leaves some individuals confused.
I think this has a little to do with the (also often implicit) distinction between research and application (ie, research vs engineering). In the context of pure research, it might make a lot of sense to take shortcuts with toy models which you could not take in the intended application of the algorithms, because you are investigating a particular phenomenon and the shortcuts don’t interfere with that investigation. However, these shortcuts can apparently change the type of the problem, and other people can become confused about what problem type you are really trying to solve.
To be a bit more concrete, you might test an AI on a toy model, and directly feed the AI some information about the toy model (as a shortcut). You can do this because the toy model is a simulation you built, so, you have direct access to it. Your intention in the research might be that such direct-fed information would be replaced with learning one day. (To you, your AI is “controller” type.) Others may misinterpret your algorithm as a search technique which takes an explicit model of a situation (they see it as “selection” type).
This could result in other people writing papers which contrast your technique with other “selection”-type techniques. Your algorithm might compare poorly because you made some decisions motivated by eventual control-type applications. This becomes hard to point out because the selection/control distinction is a bit tricky.
As far as I can see, no one there thinks search and planning are the same task.
I’m not sure what you mean about search vs planning. My guess is that search=selection and planning=control. While I do use “search” and “selection” somewhat interchangeably, I don’t want to use “planning” and “control” interchangeably; “planning” suggests a search-type operation applied to solve a control problem (the selection-process-within-a-control-process idea).
Also, it seems to me that tons of people would say that planning is a search problem, and AI textbooks tend to reflect this.
With regard to search algorithms being controllers: Here’s a discussion I had with ErickBall where they argue that planning will ultimately prove useful for search and I argue it won’t.
In the discussion, you say:
Optimization algorithms used in deep learning are typically pretty simple. Gradient descent is taught in sophomore calculus. Variants on gradient descent are typically used, but all the ones I know of are well under a page of code in complexity.
Gradient descent is extremely common these days, but much less so when I was first learning AI (just over ten years ago). To a large extent, it has turned out that “dumber” methods are easier to scale up.
However, much more sophisticated search techniques (with explicit consequentialist reasoning in the inner loop) are still discussed occasionally, especially for cases where evaluating a point is more costly. “Bayesian Optimization” is the subfield in which this is studied (that I know of). Here’s an example:
How do you reckon long-term planning will be useful for architecture search? It’s not a stateful system.
The answer (in terms of Bayesian Optimization) is that planning ahead is still helpful in the same way that planning a sequence of experiments can be helpful. You are exploring the space in order to find the best solution. At every point, you are asking “what question should I ask next, to maximize the amount of information I’ll uncover in the long run?”. This does not reduce to “what question should I ask next, in order to maximize the amount of information I have right now?”—but, most optimization algorithms don’t even go that far. Most optimization algorithms don’t explicitly reason about value-of-information at all, instead doing reasoning which is mainly designed to steer toward the best points it knows how to steer to immediately, with some randomness added in to get some exploration.
Yet, this kind of reasoning is not usually worth it, or so it seems based on the present research landscape. The overhead of planning-how-to-search is too costly; it doesn’t save time overall.
Totally agree this is a useful distinction. The map/territory thing feels right on. This is something that the mainstream AI research community doesn’t seem confused about. As far as I can see, no one there thinks search and planning are the same task.
With regard to search algorithms being controllers: Here’s a discussion I had with ErickBall where they argue that planning will ultimately prove useful for search and I argue it won’t. There might also be some new ideas for “what’s the critical distinction” in that discussion.
I guess what I think isn’t that the mainstream isn’t explicitly confused about the distinction (ie, doesn’t make confused claims), but that it isn’t clearly made/taught, which leaves some individuals confused.
I think this has a little to do with the (also often implicit) distinction between research and application (ie, research vs engineering). In the context of pure research, it might make a lot of sense to take shortcuts with toy models which you could not take in the intended application of the algorithms, because you are investigating a particular phenomenon and the shortcuts don’t interfere with that investigation. However, these shortcuts can apparently change the type of the problem, and other people can become confused about what problem type you are really trying to solve.
To be a bit more concrete, you might test an AI on a toy model, and directly feed the AI some information about the toy model (as a shortcut). You can do this because the toy model is a simulation you built, so, you have direct access to it. Your intention in the research might be that such direct-fed information would be replaced with learning one day. (To you, your AI is “controller” type.) Others may misinterpret your algorithm as a search technique which takes an explicit model of a situation (they see it as “selection” type).
This could result in other people writing papers which contrast your technique with other “selection”-type techniques. Your algorithm might compare poorly because you made some decisions motivated by eventual control-type applications. This becomes hard to point out because the selection/control distinction is a bit tricky.
I’m not sure what you mean about search vs planning. My guess is that search=selection and planning=control. While I do use “search” and “selection” somewhat interchangeably, I don’t want to use “planning” and “control” interchangeably; “planning” suggests a search-type operation applied to solve a control problem (the selection-process-within-a-control-process idea).
Also, it seems to me that tons of people would say that planning is a search problem, and AI textbooks tend to reflect this.
In the discussion, you say:
Gradient descent is extremely common these days, but much less so when I was first learning AI (just over ten years ago). To a large extent, it has turned out that “dumber” methods are easier to scale up.
However, much more sophisticated search techniques (with explicit consequentialist reasoning in the inner loop) are still discussed occasionally, especially for cases where evaluating a point is more costly. “Bayesian Optimization” is the subfield in which this is studied (that I know of). Here’s an example:
Gaussian Processes for Global Optimization (the search is framed as a sequential decision problem!)
Later, you ask:
The answer (in terms of Bayesian Optimization) is that planning ahead is still helpful in the same way that planning a sequence of experiments can be helpful. You are exploring the space in order to find the best solution. At every point, you are asking “what question should I ask next, to maximize the amount of information I’ll uncover in the long run?”. This does not reduce to “what question should I ask next, in order to maximize the amount of information I have right now?”—but, most optimization algorithms don’t even go that far. Most optimization algorithms don’t explicitly reason about value-of-information at all, instead doing reasoning which is mainly designed to steer toward the best points it knows how to steer to immediately, with some randomness added in to get some exploration.
Yet, this kind of reasoning is not usually worth it, or so it seems based on the present research landscape. The overhead of planning-how-to-search is too costly; it doesn’t save time overall.