Meta-logic might be a good theoretical framework to advance AGI a little. I don't mean that the program would have to use some sort of pure logic, I am using the term as an idea or an ideal. Meta logic does not resolve the p=?np question. However, it makes a lot of sense. It would explain how people can believe that they do one thing even though it seems obvious that they don't when you look at their actions in slightly different situations. It also explains how people can use logic to change the logic of their actions or actions of their thoughts. It explains how knowledge seems relativistic. And it explains how we can adapt to a complicated situation even though we walk around like we are blindered most of the time.
Narrow AI is powerful because a computer can run a line of narrow calculations and hold numerous previous results until they are needed. But when we think of AGI we think of problems like the recognition and search problems which are complex. Most possible results open up to numerous more possibilities and so on. A system of meta logic (literal or effective) allows an AGI program to explore numerous possibilities and then use the results of those limited explorations to change the systems of procedural logic that can be used. I believe that most AGI theories are effectively designed to act like this. The reason I am mentioning it is because I think that meta-logic makes so much sense that it should be emphasized as a simplifying theory. The theories of probability reasoning, for example, emphasizes another method of simplifying AGI problems. Thinking about a theory in a new way has some benefits similar to the formalization of a system of theories. Our computers use meta logic. Since a program has to acquire a program the logic that it uses can be acquired. The rules of the meta logic, which can be more or less general can be acquired. You don't want the program to literally forget everything it ever learned (unless you want to seriously interfere with what it is doing) but one thing that is missing in a program like Cyc is that it's effective meta-logic is almost never acquired through learning. It never learns to change its logical methods of reasoning except in a very narrow way as a carefully introduced subject reference. Isn't that the real problem of narrow AI? The effects of new ideas have to be carefully vetted or constrained in order to prevent the program from messing up what it has already learned or been programmed to do. So this idea of meta-logic is not that different from what most people in this group think of using anyway. The program goes through some kind of sequential operations and new ways to analyze the data is selected as it goes through these sequences. But rather than seeing these states just as sub-classes of all possible states, (as if the possibilities were only being filtered out as the meaning of the situation narrows in), the concept of meta-logic can be used to change the dynamics of the operations at any level of analysis. However, I also believe that this kind of system has to have cross-indexed paths that will allow it to best use the analysis that has already been done even when it has to change its path of exploration and analysis. Jim Bromer ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
