I'm currently happy with a more flexible weighted, labeled hypergraph representation scheme. I think that causation is only one among a host of different sorts of relationship that a human-like GI needs to internally represent, and I don't see a need to give it a role at the foundation of the representational scheme.
Inferred causation is important, because it drives the direct choice of actions. However, the difficulty of the assignment of credit problem (which your formalism certainly does not resolve in any way you've yet articulated), seems to imply that directly tying everything in the mind to actions via explicit causal relations, is not a feasible way to proceed in a scalable system, though it will work in toy examples... -- Ben G On Sat, Jun 9, 2012 at 3:42 PM, Sergio Pissanetzky <[email protected]>wrote: > In Computer Science, data structures are usually designed for > computational efficiency. I use a FIFO queue if I am planning to use a FIFO > algorithm, so the queue supports the algorithm efficiently. I use a > database if I need to store large amounts of information and retrieve > different views of it with the least possible amount of computer effort. * > *** > > ** ** > > Such representations suffer of one limitation: they completely divorce > data from meaning, that is, from what is sometimes called "metadata." > Database tables contain symbols, but not their meaning. If I have a > database with employees and their names and addresses, and I want the > address of a certain employee, I have to write a SQL query that references > the exact tables where that information is in. Or, write a case-specific > front end that "knows" everything that the database does not. Meaning > remains with the user. **** > > ** ** > > In Physics, representations are chosen based on their mathematical > properties, and on how well those properties represent the physics of the > system that is being represented. Efficiency in computation is not > considered. Frequently, the representations satisfy group-theoretical > requirements necessary to account for the symmetries of the physical > system. For example, I use tensors to represent mechanical systems because > tensor representations are invariant under coordinate transformations and > can adequately represent physical quantities that have magnitudes and > directions, such as position, velocity, force, and moments of inertia. I > use spinors to represent quantum systems with spin because spinors have the > adequate transformation properties. I use the Lorentz group to represent > relativistic systems again because the representation remains invariant > under transformations in spacetime. **** > > ** ** > > For complex causal systems, causal sets are the adequate representation. > Causal sets have many intrinsic properties that correspond to observed > properties of the complex systems. They have attractors, hierarchical > structures, potential wells with levels of energy, the butterfly effect, > deterministic chaos. Causal sets are isomorphic to algorithms, and as such > they can represent behavior. Any algorithm, any computer program, can be > considered as a causal set. **** > > ** ** > > With the addition of a functional that represents physical action and > corresponds to the exact point where Physics enters the pure Mathematics of > causal sets, causal sets exhibit transformations that are > behavior-preserving. These transformations are a type of inference known as > "emergent inference." Inference is any process that can derive new facts > from existing facts. Causal sets map from unstructured causal sets, of the > kind that are obtained from sensors, to structured causal sets, where the > information collected from the sensors is the existing fact and the > resulting structure is the new fact. When seen as a map, emergent inference > can be considered as a function. The function is deterministic, > uncomputable, and unpredictable. The mapping is bijective, the size of the > sets is countably infinite. There exists an inverse function, which is > deterministic and computable. The structures are the same used in > object-oriented analysis, and can be represented by UML diagrams. The > behavior-preserving transformations are equivalent to refactoring. Causal > sets also apply to models of cognition. **** > > ** ** > > Sergio**** > > ** ** > > ** ** > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Ben Goertzel, PhD http://goertzel.org "My humanity is a constant self-overcoming" -- Friedrich Nietzsche ------------------------------------------- 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
