I realize now that an example would be helpful, and yours is a good one.
Any process can be described on different levels. The trick is to find a level of description that is useful. We make an explicit effort to model actions and observation so as to separate the two directions of information flow between the agent and the environment. Actions are purely “active” (no information is received by the agent) while observations are purely “passive” (no information is sent by the agent). We do this because these two aspects of the process have very different properties, as I hope to make clear in future posts.
The process of “figuring out where the table is” involves information flowing in both directions, and so is neither an action nor an observation. Some researchers call such a thing “a subgoal”. We should break it down further, for example we could have taps as actions and echoes as observations, as you suggest.
If you want to argue that no information is lost by tapping, then fine, I won’t be pedantic and point out the tiny bits of information that do get lost. The point is that some information being lost is a representing feature of the process of taking an action. Over time, if you don’t take in new information through observations, your will have less and less information about the world, even if some actions you take have a high probability of not losing any information.
This would be true regardless of whether you engaged in any action at all, however. The passing of time since your last verification of a piece of information is that by which information is lost.
I’m assuming this model is AI-related, so my responses are going to be in line with information modeling with that in mind. If this isn’t accurate, let me know.
I would, indeed, suggest time since last verification as the mechanism in your model for information contraction, rather than action; assigning a prior probability that your information will remain accurate does a good job of completing the model. Imagine memorizing a room, closing your eyes, and firing a canon into the room. Contemporaneous to your action, your information is still valid. Shortly thereafter, it ceases to be in a rather dramatic way. Importantly for your model, I think, this is so regardless of whether you fire the canon, or another agent does. If it’s a soundproof room, and you close the door with another agent inside, your information about the state of the room can contract quite violently through no action of your own.
I realize now that an example would be helpful, and yours is a good one.
Any process can be described on different levels. The trick is to find a level of description that is useful. We make an explicit effort to model actions and observation so as to separate the two directions of information flow between the agent and the environment. Actions are purely “active” (no information is received by the agent) while observations are purely “passive” (no information is sent by the agent). We do this because these two aspects of the process have very different properties, as I hope to make clear in future posts.
The process of “figuring out where the table is” involves information flowing in both directions, and so is neither an action nor an observation. Some researchers call such a thing “a subgoal”. We should break it down further, for example we could have taps as actions and echoes as observations, as you suggest.
If you want to argue that no information is lost by tapping, then fine, I won’t be pedantic and point out the tiny bits of information that do get lost. The point is that some information being lost is a representing feature of the process of taking an action. Over time, if you don’t take in new information through observations, your will have less and less information about the world, even if some actions you take have a high probability of not losing any information.
This would be true regardless of whether you engaged in any action at all, however. The passing of time since your last verification of a piece of information is that by which information is lost.
I’m assuming this model is AI-related, so my responses are going to be in line with information modeling with that in mind. If this isn’t accurate, let me know.
I would, indeed, suggest time since last verification as the mechanism in your model for information contraction, rather than action; assigning a prior probability that your information will remain accurate does a good job of completing the model. Imagine memorizing a room, closing your eyes, and firing a canon into the room. Contemporaneous to your action, your information is still valid. Shortly thereafter, it ceases to be in a rather dramatic way. Importantly for your model, I think, this is so regardless of whether you fire the canon, or another agent does. If it’s a soundproof room, and you close the door with another agent inside, your information about the state of the room can contract quite violently through no action of your own.