SNAP: parallel processing applied to AI

It is argued that a viable solution for building future intelligent systems is to design special-purpose parallel computer architectures. The applications are restricted to those using semantic networks for knowledge representation. Reasoning on these networks is achieved with a marker-passing model of processing. The Semantic Network Array Processor (SNAP), a marker-passing parallel computer dedicated for natural-language and other knowledge-processing applications, is considered. Solutions for several nontrivial natural-language problems using the marker-passing approach are discussed.<
>