Thinking Out Loud

This post is mostly for me to organize my thoughts, but I thought it might be interesting to share.

One aspect that isn't covered very well in my research is the cross-over from long-term memory to working memory. That is, I've read a lot about general knowledge representations, but much less about instantiating those concepts when it comes to actual objects in the environment. The book "Explorations in Cognition" talks a bit about the difference between the "Mental World" and the "Real World", which is nearly analogous - the difference is that the mental world in that case is what is expected, whereas I treat the "mental world" as common-sense long-term knowledge.

Objects in the environment pose a couple of challenges. First, how do we determine whether they are in the environment? We can have certain senses, but individual senses may not be enough to completely identify an object. For the sake of this project, we can assume that all required information is made immediately available in the small world. But even then, there's an important property of properties - whether they are satisfied or not.

In a knowledge base, an unsatisfied property means that an object has a range of possible values for a particular property. For example, an apple can be red, green, or yellow. Therefore, in our knowledge base, apple has the unsatisfied property of color, with a range of values - red, green, yellow. These properties can be satisfied by specifying the type of apple. We say a Granny Smith IS-A apple, and it satisfies the color property by setting its value to green. We could also specify that we're talking about a "red apple", and that instance would have its property satisfied.

But what if the object that could satisfy a property isn't satisfied itself? Well then the property isn't satisfied, technically. A car is a vehicle that has wheels - that seems to satisfy the method of propulsion (or whatever you want to call it). But we don't know what kind of wheel it is, what it looks like, etc. So we can't give a full representation of what a car is without this extra information.

So say we have a box in our small environment. We know a box is an object, and it satisfies the property of "shape" with the value "cube", and it will have a color that is unique to that instance. The shape property will be connected to the concept node of "box", while the color property will be connected to the instance node of that particular box. If we want to get a property of a particular instance, we first look at the instance itself for the property, then we can move up the conceptual hierarchy (following IS-A edges) to continue looking.

I think I'm going to revise my plan from my last post. Instead of looking for a property that can fulfill the argument and trying to satisfy it immediately, I'm going to find the instance node that corresponds to the object in question. From there, I can run the ArgumentIntersector with an activator at the concept and the property that is required.

I think that's it for now.