Hi,
It seems that what you are saying, though, is that a KR must involve "probabilities in some shape or form" and "the ability of a representation to jump up a level and represent/manipulate other representations, not just represent the world".
Yes, and these two aspects must work together so that it can sensibly apply estimate probabilistics associated with higher-order functions/representations... What I am saying is not just that a KR must be capable of these two things, but that these should be implemented at the "low level" of a KR rather than as high-level abstractions ... i.e., the KR must permit the cognitive system an extremely easy and ready facility at using these, so that it can use them in representing nearly everything it has to represent, and to manipulate them (probabilities and higher-order functions) very freely and flexibly and efficiently... For instance, a crisp predicate logic based KR is in principle capable of handling probabilities, by representing them as logical structures, but it contains no low-level way of representing probabilities, and therefore doing uncertain inference in such a KR tends to be awkward and inefficient.... Similarly, most NN architectures contain no explicit way to take little NN's and encode them as inputs to other NN's. So implementing complex higher-order functions in the context of most NN architectures is not really feasible, even though in principle possible by creating appropriate networks So, actually, I would say the very simple criteria I mentioned rule out nearly all KR's in currency in the AI field ;-) Having said that, I do think they are somewhat obvious, from a general cognitive systems point of view, yeah...
But you seemed to be saying something much stronger when you used the phrase "... it must be sensibly viewable as a probabilistic logic based functional programming language." I can think of huge numbers of ways to satisfy the weak claims, above, but this latter is just one specific choice, and I see nothing compelling me to accept anything remotely like a probabilistic logic based functional programming language.
My claim is that any KR that implements probabilities and higher order functions at a sufficiently low level that they can be flexibly and adaptively deployed as needed to create new specialized representations for new situations -- will be relatively easily **translatable** into the form of a probabilistic logic based functional programming language... For example, I conjecture that the KR implemented by neuronal assemblies and networks theoreof in the brain will be easily and cleanly translatable into such a form, once the brain is really well understood... This is certainly not an empty claim. For instance, it is different from the claim that --- the KR implemented by neuronal assemblies and networks theoreof in the brain will be easily and cleanly translatable into classical Prolog --- the KR implemented by neuronal assemblies and networks theoreof in the brain will be easily and cleanly translatable into crisp predicate logic --- the KR implemented by neuronal assemblies and networks theoreof in the brain will be easily and cleanly translatable into a giant network of feedforward NN's and Kohonen nets etc. It is a specific claim about what sort of KR's are going to be most useful for general intelligence.... Not a mathematically rigorous claim (because I have not formally defined "easily and cleanly translatable", though...) -- Ben -- Ben ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/[EMAIL PROTECTED]
