Re: [agi] How do we Score Hypotheses?
It is no wonder that I'm having a hard time finding documentation on hypothesis scoring. Few can agree on how to do it and there is much debate about it. I noticed though that a big reason for the problems is that explanatory reasoning is being applied to many diverse problems. I think, like I mentioned before, that people should not try to come up with a single universal rule set for applying explanatory reasoning to every possible problem. So, maybe that's where the hold up is. I've been testing my ideas out on complex examples. But now I'm going to go back to simplified model testing (although not as simple as black squares :) ) and work my way up again. Dave On Wed, Jul 14, 2010 at 12:59 PM, David Jones davidher...@gmail.com wrote: Actually, I just realized that there is a way to included inductive knowledge and experience into this algorithm. Inductive knowledge and experience about a specific object or object type can be exploited to know which hypotheses in the past were successful, and therefore which hypothesis is most likely. By choosing the most likely hypothesis first, we skip a lot of messy hypothesis comparison processing and analysis. If we choose the right hypothesis first, all we really have to do is verify that this hypothesis reveals in the data what we expect to be there. If we confirm what we expect, that is reason enough not to look for other hypotheses because the data is explained by what we originally believed to be likely. We only look for additional hypotheses when we find something unexplained. And even then, we don't look at the whole problem. We only look at what we have to to explain the unexplained data. In fact, we could even ignore the unexplained data if we believe, from experience, that it isn't pertinent. I discovered this because I'm analyzing how a series of hypotheses are navigated when analyzing images. It seems to me that it is done very similarly to way we do it. We sort of confirm what we expect and try to explain what we don't expect. We try out hypotheses in a sort of trial and error manor and see how each hypothesis affects what we find in the image. If we confirm things because of the hypothesis, we are likely to keep it. We keep going, navigating the tree of hypotheses, conflicts and unexpected observations until we find a good hypothesis. Something like that. I'm attempting to construct an algorithm for doing this as I analyze specific problems. Dave On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.comwrote: What do you mean by definitive events? I guess the first problem I see with my approach is that the movement of the window is also a hypothesis. I need to analyze it in more detail and see how the tree of hypotheses affects the hypotheses regarding the es on the windows. What I believe is that these problems can be broken down into types of hypotheses, types of events and types of relationships. then those types can be reasoned about in a general way. If possible, then you have a method for reasoning about any object that is covered by the types of hypotheses, events and relationships that you have defined. How to reason about specific objects should not be preprogrammed. But, I think the solution to this part of AGI is to find general ways to reason about a small set of concepts that can be combined to describe specific objects and situations. There are other parts to AGI that I am not considering yet. I believe the problem has to be broken down into separate pieces and understood before putting it back together into a complete system. I have not covered inductive learning for example, which would be an important part of AGI. I have also not yet incorporated learned experience into the algorithm, which is also important. The general AI problem is way too complicated to consider all at once. I simply can't solve hypothesis generation, comparison and disambiguation while at the same time solving induction and experience-based reasoning. It becomes unwieldly. So, I'm starting where I can and I'll work my way up to the full complexity of the problem. I don't really understand what you mean here: The central unsolved problem, in my view, is: How can hypotheses be conceptually integrated along with the observable definitive events of the problem to form good explanatory connections that can mesh well with other knowledge about the problem that is considered to be reliable. The second problem is finding efficient ways to represent this complexity of knowledge so that the program can utilize it efficiently. You also might want to include concrete problems to analyze for your central problem suggestions. That would help define the problem a bit better for analysis. Dave On Wed, Jul 14, 2010 at 8:30 AM, Jim Bromer jimbro...@gmail.com wrote: On Tue, Jul 13, 2010 at 9:05 PM, Jim Bromer jimbro...@gmail.com wrote: Even if you refined your model until it was just
Re: [agi] How do we Score Hypotheses?
Hypotheses are scored using Bayes law. Let D be your observed data and H be your hypothesis. Then p(H|D) = p(D|H)p(H)/p(D). Since p(D) is constant, you can remove it and rank hypotheses by p(D|H)p(H). p(H) can be estimated using the minimum description length principle or Solomonoff induction. Ideally, p(H) = 2^-|H| where |H| is the length (in bits) of the description of the hypothesis. The value is language dependent, so this method is not perfect. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Thu, July 15, 2010 10:22:44 AM Subject: Re: [agi] How do we Score Hypotheses? It is no wonder that I'm having a hard time finding documentation on hypothesis scoring. Few can agree on how to do it and there is much debate about it. I noticed though that a big reason for the problems is that explanatory reasoning is being applied to many diverse problems. I think, like I mentioned before, that people should not try to come up with a single universal rule set for applying explanatory reasoning to every possible problem. So, maybe that's where the hold up is. I've been testing my ideas out on complex examples. But now I'm going to go back to simplified model testing (although not as simple as black squares :) ) and work my way up again. Dave On Wed, Jul 14, 2010 at 12:59 PM, David Jones davidher...@gmail.com wrote: Actually, I just realized that there is a way to included inductive knowledge and experience into this algorithm. Inductive knowledge and experience about a specific object or object type can be exploited to know which hypotheses in the past were successful, and therefore which hypothesis is most likely. By choosing the most likely hypothesis first, we skip a lot of messy hypothesis comparison processing and analysis. If we choose the right hypothesis first, all we really have to do is verify that this hypothesis reveals in the data what we expect to be there. If we confirm what we expect, that is reason enough not to look for other hypotheses because the data is explained by what we originally believed to be likely. We only look for additional hypotheses when we find something unexplained. And even then, we don't look at the whole problem. We only look at what we have to to explain the unexplained data. In fact, we could even ignore the unexplained data if we believe, from experience, that it isn't pertinent. I discovered this because I'm analyzing how a series of hypotheses are navigated when analyzing images. It seems to me that it is done very similarly to way we do it. We sort of confirm what we expect and try to explain what we don't expect. We try out hypotheses in a sort of trial and error manor and see how each hypothesis affects what we find in the image. If we confirm things because of the hypothesis, we are likely to keep it. We keep going, navigating the tree of hypotheses, conflicts and unexpected observations until we find a good hypothesis. Something like that. I'm attempting to construct an algorithm for doing this as I analyze specific problems. Dave On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.com wrote: What do you mean by definitive events? I guess the first problem I see with my approach is that the movement of the window is also a hypothesis. I need to analyze it in more detail and see how the tree of hypotheses affects the hypotheses regarding the es on the windows. What I believe is that these problems can be broken down into types of hypotheses, types of events and types of relationships. then those types can be reasoned about in a general way. If possible, then you have a method for reasoning about any object that is covered by the types of hypotheses, events and relationships that you have defined. How to reason about specific objects should not be preprogrammed. But, I think the solution to this part of AGI is to find general ways to reason about a small set of concepts that can be combined to describe specific objects and situations. There are other parts to AGI that I am not considering yet. I believe the problem has to be broken down into separate pieces and understood before putting it back together into a complete system. I have not covered inductive learning for example, which would be an important part of AGI. I have also not yet incorporated learned experience into the algorithm, which is also important. The general AI problem is way too complicated to consider all at once. I simply can't solve hypothesis generation, comparison and disambiguation while at the same time solving induction and experience-based reasoning. It becomes unwieldly. So, I'm starting where I can and I'll work my way up to the full complexity of the problem. I don't really understand what you mean here: The central unsolved problem, in my view, is: How can hypotheses
Re: [agi] How do we Score Hypotheses?
:) You say that as if bayesian explanatory reasoning is the only way. There is much debate over bayesian explanatory reasoning and non-bayesian. There are pros and cons to bayesian methods. Likewise, there is the problem with non-bayesian methods because few have figured out how to do it effectively. I'm still going to pursue a non-bayesian approach because I believe there is likely more merit to it and that the short-comings can be overcome. Dave On Thu, Jul 15, 2010 at 10:54 AM, Matt Mahoney matmaho...@yahoo.com wrote: Hypotheses are scored using Bayes law. Let D be your observed data and H be your hypothesis. Then p(H|D) = p(D|H)p(H)/p(D). Since p(D) is constant, you can remove it and rank hypotheses by p(D|H)p(H). p(H) can be estimated using the minimum description length principle or Solomonoff induction. Ideally, p(H) = 2^-|H| where |H| is the length (in bits) of the description of the hypothesis. The value is language dependent, so this method is not perfect. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Thu, July 15, 2010 10:22:44 AM *Subject:* Re: [agi] How do we Score Hypotheses? It is no wonder that I'm having a hard time finding documentation on hypothesis scoring. Few can agree on how to do it and there is much debate about it. I noticed though that a big reason for the problems is that explanatory reasoning is being applied to many diverse problems. I think, like I mentioned before, that people should not try to come up with a single universal rule set for applying explanatory reasoning to every possible problem. So, maybe that's where the hold up is. I've been testing my ideas out on complex examples. But now I'm going to go back to simplified model testing (although not as simple as black squares :) ) and work my way up again. Dave On Wed, Jul 14, 2010 at 12:59 PM, David Jones davidher...@gmail.comwrote: Actually, I just realized that there is a way to included inductive knowledge and experience into this algorithm. Inductive knowledge and experience about a specific object or object type can be exploited to know which hypotheses in the past were successful, and therefore which hypothesis is most likely. By choosing the most likely hypothesis first, we skip a lot of messy hypothesis comparison processing and analysis. If we choose the right hypothesis first, all we really have to do is verify that this hypothesis reveals in the data what we expect to be there. If we confirm what we expect, that is reason enough not to look for other hypotheses because the data is explained by what we originally believed to be likely. We only look for additional hypotheses when we find something unexplained. And even then, we don't look at the whole problem. We only look at what we have to to explain the unexplained data. In fact, we could even ignore the unexplained data if we believe, from experience, that it isn't pertinent. I discovered this because I'm analyzing how a series of hypotheses are navigated when analyzing images. It seems to me that it is done very similarly to way we do it. We sort of confirm what we expect and try to explain what we don't expect. We try out hypotheses in a sort of trial and error manor and see how each hypothesis affects what we find in the image. If we confirm things because of the hypothesis, we are likely to keep it. We keep going, navigating the tree of hypotheses, conflicts and unexpected observations until we find a good hypothesis. Something like that. I'm attempting to construct an algorithm for doing this as I analyze specific problems. Dave On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.comwrote: What do you mean by definitive events? I guess the first problem I see with my approach is that the movement of the window is also a hypothesis. I need to analyze it in more detail and see how the tree of hypotheses affects the hypotheses regarding the es on the windows. What I believe is that these problems can be broken down into types of hypotheses, types of events and types of relationships. then those types can be reasoned about in a general way. If possible, then you have a method for reasoning about any object that is covered by the types of hypotheses, events and relationships that you have defined. How to reason about specific objects should not be preprogrammed. But, I think the solution to this part of AGI is to find general ways to reason about a small set of concepts that can be combined to describe specific objects and situations. There are other parts to AGI that I am not considering yet. I believe the problem has to be broken down into separate pieces and understood before putting it back together into a complete system. I have not covered inductive learning for example, which would be an important part of AGI. I have also not yet incorporated
Re: [agi] How do we Score Hypotheses?
On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.com wrote: What do you mean by definitive events? I was just trying to find a way to designate obsverations that would be reliably obvious to a computer program. This has something to do with the assumptions that you are using. For example if some object appeared against a stable background and it was a different color than the background, it would be a definitive observation event because your algorithm could detect it with some certainty and use it in the definition of other more complicated events (like occlusion.) Notice that this example would not necessarily be so obvious (a definitive event) using a camera, because there are a number of ways that an illusion (of some kind) could end up as a data event. I will try to reply to the rest of your message sometime later. Jim Bromer --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] How do we Score Hypotheses?
Jim, even that isn't an obvious event. You don't know what is background and what is not. You don't even know if there is an object or not. You don't know if anything moved or not. You can make some observations using predefined methods and then see if you find matches... then hypothesize about the matches... It all has to be learned and figured out through reasoning. That's why I asked what you meant by definitive events. Nothing is really definitive. It is all hypothesized in a non-monotonic manner. Dave On Thu, Jul 15, 2010 at 12:01 PM, Jim Bromer jimbro...@gmail.com wrote: On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.comwrote: What do you mean by definitive events? I was just trying to find a way to designate obsverations that would be reliably obvious to a computer program. This has something to do with the assumptions that you are using. For example if some object appeared against a stable background and it was a different color than the background, it would be a definitive observation event because your algorithm could detect it with some certainty and use it in the definition of other more complicated events (like occlusion.) Notice that this example would not necessarily be so obvious (a definitive event) using a camera, because there are a number of ways that an illusion (of some kind) could end up as a data event. I will try to reply to the rest of your message sometime later. Jim Bromer *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] How do we Score Hypotheses?
Sounds like a good explanation of why a body is essential for vision - not just for POV and orientation [up/left/right/down/ towards/ away] but for comparison and yardstick - you do know when your body or parts thereof are moving -and it's not merely touch but the comparison of other objects still and moving with your own moving hands and body that is important. The more you go into it, the crazier the prospect of vision without eyes in a body becomes. From: David Jones Sent: Thursday, July 15, 2010 5:54 PM To: agi Subject: Re: [agi] How do we Score Hypotheses? Jim, even that isn't an obvious event. You don't know what is background and what is not. You don't even know if there is an object or not. You don't know if anything moved or not. You can make some observations using predefined methods and then see if you find matches... then hypothesize about the matches... It all has to be learned and figured out through reasoning. That's why I asked what you meant by definitive events. Nothing is really definitive. It is all hypothesized in a non-monotonic manner. Dave On Thu, Jul 15, 2010 at 12:01 PM, Jim Bromer jimbro...@gmail.com wrote: On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.com wrote: What do you mean by definitive events? I was just trying to find a way to designate obsverations that would be reliably obvious to a computer program. This has something to do with the assumptions that you are using. For example if some object appeared against a stable background and it was a different color than the background, it would be a definitive observation event because your algorithm could detect it with some certainty and use it in the definition of other more complicated events (like occlusion.) Notice that this example would not necessarily be so obvious (a definitive event) using a camera, because there are a number of ways that an illusion (of some kind) could end up as a data event. I will try to reply to the rest of your message sometime later. Jim Bromer agi | Archives | Modify Your Subscription agi | Archives | Modify Your Subscription --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] How do we Score Hypotheses?
On screenshots, the point of view is equivalent to the absolute positions and their relative positions using absolute(screen x and y) measurements. You don't need a robot to learn about how AGI works and figure out how to solve some problems. It would be a terrible mistake to spend years, or even weeks for that matter, on robotics before getting started. Dave On Thu, Jul 15, 2010 at 1:09 PM, Mike Tintner tint...@blueyonder.co.ukwrote: Sounds like a good explanation of why a body is essential for vision - not just for POV and orientation [up/left/right/down/ towards/ away] but for comparison and yardstick - you do know when your body or parts thereof are moving -and it's not merely touch but the comparison of other objects still and moving with your own moving hands and body that is important. The more you go into it, the crazier the prospect of vision without eyes in a body becomes. *From:* David Jones davidher...@gmail.com *Sent:* Thursday, July 15, 2010 5:54 PM *To:* agi agi@v2.listbox.com *Subject:* Re: [agi] How do we Score Hypotheses? Jim, even that isn't an obvious event. You don't know what is background and what is not. You don't even know if there is an object or not. You don't know if anything moved or not. You can make some observations using predefined methods and then see if you find matches... then hypothesize about the matches... It all has to be learned and figured out through reasoning. That's why I asked what you meant by definitive events. Nothing is really definitive. It is all hypothesized in a non-monotonic manner. Dave On Thu, Jul 15, 2010 at 12:01 PM, Jim Bromer jimbro...@gmail.com wrote: On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.comwrote: What do you mean by definitive events? I was just trying to find a way to designate obsverations that would be reliably obvious to a computer program. This has something to do with the assumptions that you are using. For example if some object appeared against a stable background and it was a different color than the background, it would be a definitive observation event because your algorithm could detect it with some certainty and use it in the definition of other more complicated events (like occlusion.) Notice that this example would not necessarily be so obvious (a definitive event) using a camera, because there are a number of ways that an illusion (of some kind) could end up as a data event. I will try to reply to the rest of your message sometime later. Jim Bromer *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] How do we Score Hypotheses?
On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.com wrote: I don't really understand what you mean here: The central unsolved problem, in my view, is: How can hypotheses be conceptually integrated along with the observable definitive events of the problem to form good explanatory connections that can mesh well with other knowledge about the problem that is considered to be reliable. The second problem is finding efficient ways to represent this complexity of knowledge so that the program can utilize it efficiently. You also might want to include concrete problems to analyze for your central problem suggestions. That would help define the problem a bit better for analysis. Dave I suppose a hypotheses is a kind of concepts. So there are other kinds of concepts that we need to use with hypotheses. A hypotheses has to be conceptually integrated into other concepts. Conceptual integration is something of greater complexity than shallow deduction or probability chains. While reasoning chains are needed in conceptual integration, conceptual integration is to a chain of reasoning what a multi dimension structure is to a one dimensional chain. I will try to come up with some examples. Jim Bromer --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] How do we Score Hypotheses?
On Tue, Jul 13, 2010 at 9:05 PM, Jim Bromer jimbro...@gmail.com wrote: Even if you refined your model until it was just right, you would have only caught up to everyone else with a solution to a narrow AI problem. I did not mean that you would just have a solution to a narrow AI problem, but that your solution, if put in the form of scoring of points on the basis of the observation *of definitive* events, would constitute a narrow AI method. The central unsolved problem, in my view, is: How can hypotheses be conceptually integrated along with the observable definitive events of the problem to form good explanatory connections that can mesh well with other knowledge about the problem that is considered to be reliable. The second problem is finding efficient ways to represent this complexity of knowledge so that the program can utilize it efficiently. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] How do we Score Hypotheses?
What do you mean by definitive events? I guess the first problem I see with my approach is that the movement of the window is also a hypothesis. I need to analyze it in more detail and see how the tree of hypotheses affects the hypotheses regarding the es on the windows. What I believe is that these problems can be broken down into types of hypotheses, types of events and types of relationships. then those types can be reasoned about in a general way. If possible, then you have a method for reasoning about any object that is covered by the types of hypotheses, events and relationships that you have defined. How to reason about specific objects should not be preprogrammed. But, I think the solution to this part of AGI is to find general ways to reason about a small set of concepts that can be combined to describe specific objects and situations. There are other parts to AGI that I am not considering yet. I believe the problem has to be broken down into separate pieces and understood before putting it back together into a complete system. I have not covered inductive learning for example, which would be an important part of AGI. I have also not yet incorporated learned experience into the algorithm, which is also important. The general AI problem is way too complicated to consider all at once. I simply can't solve hypothesis generation, comparison and disambiguation while at the same time solving induction and experience-based reasoning. It becomes unwieldly. So, I'm starting where I can and I'll work my way up to the full complexity of the problem. I don't really understand what you mean here: The central unsolved problem, in my view, is: How can hypotheses be conceptually integrated along with the observable definitive events of the problem to form good explanatory connections that can mesh well with other knowledge about the problem that is considered to be reliable. The second problem is finding efficient ways to represent this complexity of knowledge so that the program can utilize it efficiently. You also might want to include concrete problems to analyze for your central problem suggestions. That would help define the problem a bit better for analysis. Dave On Wed, Jul 14, 2010 at 8:30 AM, Jim Bromer jimbro...@gmail.com wrote: On Tue, Jul 13, 2010 at 9:05 PM, Jim Bromer jimbro...@gmail.com wrote: Even if you refined your model until it was just right, you would have only caught up to everyone else with a solution to a narrow AI problem. I did not mean that you would just have a solution to a narrow AI problem, but that your solution, if put in the form of scoring of points on the basis of the observation *of definitive* events, would constitute a narrow AI method. The central unsolved problem, in my view, is: How can hypotheses be conceptually integrated along with the observable definitive events of the problem to form good explanatory connections that can mesh well with other knowledge about the problem that is considered to be reliable. The second problem is finding efficient ways to represent this complexity of knowledge so that the program can utilize it efficiently. *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] How do we Score Hypotheses?
Actually, I just realized that there is a way to included inductive knowledge and experience into this algorithm. Inductive knowledge and experience about a specific object or object type can be exploited to know which hypotheses in the past were successful, and therefore which hypothesis is most likely. By choosing the most likely hypothesis first, we skip a lot of messy hypothesis comparison processing and analysis. If we choose the right hypothesis first, all we really have to do is verify that this hypothesis reveals in the data what we expect to be there. If we confirm what we expect, that is reason enough not to look for other hypotheses because the data is explained by what we originally believed to be likely. We only look for additional hypotheses when we find something unexplained. And even then, we don't look at the whole problem. We only look at what we have to to explain the unexplained data. In fact, we could even ignore the unexplained data if we believe, from experience, that it isn't pertinent. I discovered this because I'm analyzing how a series of hypotheses are navigated when analyzing images. It seems to me that it is done very similarly to way we do it. We sort of confirm what we expect and try to explain what we don't expect. We try out hypotheses in a sort of trial and error manor and see how each hypothesis affects what we find in the image. If we confirm things because of the hypothesis, we are likely to keep it. We keep going, navigating the tree of hypotheses, conflicts and unexpected observations until we find a good hypothesis. Something like that. I'm attempting to construct an algorithm for doing this as I analyze specific problems. Dave On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.com wrote: What do you mean by definitive events? I guess the first problem I see with my approach is that the movement of the window is also a hypothesis. I need to analyze it in more detail and see how the tree of hypotheses affects the hypotheses regarding the es on the windows. What I believe is that these problems can be broken down into types of hypotheses, types of events and types of relationships. then those types can be reasoned about in a general way. If possible, then you have a method for reasoning about any object that is covered by the types of hypotheses, events and relationships that you have defined. How to reason about specific objects should not be preprogrammed. But, I think the solution to this part of AGI is to find general ways to reason about a small set of concepts that can be combined to describe specific objects and situations. There are other parts to AGI that I am not considering yet. I believe the problem has to be broken down into separate pieces and understood before putting it back together into a complete system. I have not covered inductive learning for example, which would be an important part of AGI. I have also not yet incorporated learned experience into the algorithm, which is also important. The general AI problem is way too complicated to consider all at once. I simply can't solve hypothesis generation, comparison and disambiguation while at the same time solving induction and experience-based reasoning. It becomes unwieldly. So, I'm starting where I can and I'll work my way up to the full complexity of the problem. I don't really understand what you mean here: The central unsolved problem, in my view, is: How can hypotheses be conceptually integrated along with the observable definitive events of the problem to form good explanatory connections that can mesh well with other knowledge about the problem that is considered to be reliable. The second problem is finding efficient ways to represent this complexity of knowledge so that the program can utilize it efficiently. You also might want to include concrete problems to analyze for your central problem suggestions. That would help define the problem a bit better for analysis. Dave On Wed, Jul 14, 2010 at 8:30 AM, Jim Bromer jimbro...@gmail.com wrote: On Tue, Jul 13, 2010 at 9:05 PM, Jim Bromer jimbro...@gmail.com wrote: Even if you refined your model until it was just right, you would have only caught up to everyone else with a solution to a narrow AI problem. I did not mean that you would just have a solution to a narrow AI problem, but that your solution, if put in the form of scoring of points on the basis of the observation *of definitive* events, would constitute a narrow AI method. The central unsolved problem, in my view, is: How can hypotheses be conceptually integrated along with the observable definitive events of the problem to form good explanatory connections that can mesh well with other knowledge about the problem that is considered to be reliable. The second problem is finding efficient ways to represent this complexity of knowledge so that the program can utilize it efficiently. *agi* |
[agi] How do we Score Hypotheses?
I've been trying to figure out how to score hypotheses. Do you guys have any constructive ideas about how to define the way you score hypotheses like these a little better? I'll define the problem below in detail. I know Abram mentioned MDL, which I'm about to look into. Does that even apply to this sort of thing? I came up with a hypothesis scoring idea. It goes as follows *Rule 1:* Hypotheses are compared only 1 at a time. *Rule 2:* If hypothesis 1 predicts/expects/anticipates something, then you add (+1) to its score and subtract (-1) from hypothesis 2 if it doesn't also anticipate the observation. (Note:When comparing only 2 hypotheses, it may actually not be necessary to subtract from the competing hypothesis I guess.) *Here is the specific problem I'm analyzing: *Let's say that you have two window objects that contain the same letter, such as the letter e. In frame 0, the first window object is visible. In frame 1, window 1 moves a bit. In frame 2 though, the second window object appears and completely occludes the first window object. So, if you only look at the letter e from frame 0 to frame 2, it looks like it never disappears and it just moves. But that's not what happens. There are two independent instances of the letter e. But, how do we get the algorithm to figure this out in a general way? How do we get it to compare the two possible hypotheses (1 object or two objects) and decide that one is better than the other? That is what the hypothesis scoring method is for. *Algorithm Description and Details* *Hypothesis 1:* there are two separate objects... there are two separate instances of the letter e *Hypothesis 2:* there is only one letter object... only one letter e that occurs in all the frames of the video. *Time 0: object 1* *Time 1: e moves rigidly with object 1* H1: +1 compared to h2 because we expect the e to move rigidly with the first object, rather than independently from the first object. H2: -1 compared to h1 because we don't expect the first object to move rigidly with e but h1 does. *Time 2: object 2 appears and completely occludes object 1. Object 1 and 2 both have the letter e on them. So, to a dumb algorithm, it looks as if the e moved between the two frames of the video.* H1: -1 compared to h2 because we don't expect what h2 expects. H2: +1 compared to h1 e moves independently of the first window *Time 3: e moves rigidly with object 2* H1: +1 compared to h2 e moves with second object. H2: -1 compared to h1 *Time 4: e moves rigidly with object 2* H1: +1 compared to h2 e moves with second object. H2: -1 compared to h1 *Time 5: e moves rigidly with object 2* H1: +1 compared to h2 e moves with second object. H2: -1 compared to h1 *After 5 video frames the score is: * H1: +3 H2: -3 Dave --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] How do we Score Hypotheses?
My opinion is that this is as good a place to start as any. At least you are dealing with an actual problem, your trying different stuff out, and you seem like you are willing to actually try it out. The problem is that the scoring is based on a superficial model of conceptual integration, where, for some reason, you believe that the answer to the essential problem includes a method of rephrasing the problem into simpler questions which then can magically be answered. You are worried about the finery without first creating the structure. Even if you refined your model until it was just right, you would have only caught up to everyone else with a solution to a narrow AI problem. Jim Bromer On Tue, Jul 13, 2010 at 8:15 PM, David Jones davidher...@gmail.com wrote: I've been trying to figure out how to score hypotheses. Do you guys have any constructive ideas about how to define the way you score hypotheses like these a little better? I'll define the problem below in detail. I know Abram mentioned MDL, which I'm about to look into. Does that even apply to this sort of thing? I came up with a hypothesis scoring idea. It goes as follows *Rule 1:* Hypotheses are compared only 1 at a time. *Rule 2:* If hypothesis 1 predicts/expects/anticipates something, then you add (+1) to its score and subtract (-1) from hypothesis 2 if it doesn't also anticipate the observation. (Note:When comparing only 2 hypotheses, it may actually not be necessary to subtract from the competing hypothesis I guess.) *Here is the specific problem I'm analyzing: *Let's say that you have two window objects that contain the same letter, such as the letter e. In frame 0, the first window object is visible. In frame 1, window 1 moves a bit. In frame 2 though, the second window object appears and completely occludes the first window object. So, if you only look at the letter e from frame 0 to frame 2, it looks like it never disappears and it just moves. But that's not what happens. There are two independent instances of the letter e. But, how do we get the algorithm to figure this out in a general way? How do we get it to compare the two possible hypotheses (1 object or two objects) and decide that one is better than the other? That is what the hypothesis scoring method is for. *Algorithm Description and Details* *Hypothesis 1:* there are two separate objects... there are two separate instances of the letter e *Hypothesis 2:* there is only one letter object... only one letter e that occurs in all the frames of the video. *Time 0: object 1* *Time 1: e moves rigidly with object 1* H1: +1 compared to h2 because we expect the e to move rigidly with the first object, rather than independently from the first object. H2: -1 compared to h1 because we don't expect the first object to move rigidly with e but h1 does. *Time 2: object 2 appears and completely occludes object 1. Object 1 and 2 both have the letter e on them. So, to a dumb algorithm, it looks as if the e moved between the two frames of the video.* H1: -1 compared to h2 because we don't expect what h2 expects. H2: +1 compared to h1 e moves independently of the first window *Time 3: e moves rigidly with object 2* H1: +1 compared to h2 e moves with second object. H2: -1 compared to h1 *Time 4: e moves rigidly with object 2* H1: +1 compared to h2 e moves with second object. H2: -1 compared to h1 *Time 5: e moves rigidly with object 2* H1: +1 compared to h2 e moves with second object. H2: -1 compared to h1 *After 5 video frames the score is: * H1: +3 H2: -3 Dave *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com/ --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
