The HaveNWant Common Cortical Algorithm

HaveNWant is a low-level cognitive schemata that processes bits of information bidirectionally. It is a common cortical algorithm whose Tinker-Toy® like parts can construct networks that react powerfully in embedded environments. They exhibit many of the animal learning abilities described by Piaget. Learning occurs when unknowns are detected, triggering the addition of new elements to the network. In this way, forward models can be built from experience, and many of these models can be linked together to form large distributed associative memories; HaveNWant's level of abstraction lies well above neurological models, focusing on functionality and avoiding biological constraints. It also lies above computer AND and OR gates, which operate unidirectionally. In HaveNWant, for every signal going one way, there is another signal coming back. HaveNWant atoms continually reconcile the information on their links, each imposing a particular constraint. Its networks aggregate many single links, to efficiently enforce large sophisticated relationships. HaveNWant operates below most AI architectures, which have algorithms that do not constrain bit-level computational locality. Although the examples given involve toy networks, we have a plan to extend the base algorithms by adding dynamically learned variables and noise tolerance, to produce robust behavior.