Well, I am still sceptical of the theory for one basic reason. The problem, as I see it, is that the complexity of the level of general knowledge that is (probably) required for a basic AGI program is too great for any of these methods to work with anything other than superficial or at best simple examples. So if the day came when one method worked then a great many methods would probably work because it would mean that someone had figured out how to contain combinatorial complexity or had developed hardware sufficient to attain minimal AI.
Weighted reasoning was at first believed to be a solution to combinatorial complexity. Why didn't it work? There were a few problems. One was that not everything can be expressed in the terms of a range of values. Secondly, a weighting would, by the very method that it is able to simplify complex relationships, represent different kinds of things and these different things get mushed up and produce sub par results. Finally, these systems have no way to integrate different kinds of relations wisely. Of course, since weighted reasoning is usually used with correlations, one might imagine that correlation points could be developed to represent when these complicated relations can be fused and when they should be divided and how they can be structured and integrated. This would require some trial and error methods to learn how to apply these techniques to real world modelling, but few people actually talk about stuff like this. And there is no reason to think that this sort of method can produce intelligence without first transcending the combinatorial complexity problem. I have thought about taking actions that can be used to minimize the complications of numerous unknowns, but since this strategy has to be based on some method of avoiding the worse outcomes, that means that the strategy cannot be based on a simplistic way to minimize "entropy". Taking an action when facing multiple unknowns has to be derived from a biased method that help the entity avoid the worse outcomes. This in turn implies that biasing strategies could also be used in the hope of increasing the chances of better outcomes based on the projection of insights about the kinds of situation that the intelligent device thought it might be in. Jim Bromer ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
