A Class Library Implementation of a Principled Open Architecture Knowledge Representation Server with Plug-in Data Types

摘要

A knowledge representation server is described which provides a fast, memory-efficient and principled system component. Modeling the server through intensional algebraic semantics leads naturally to an open architecture class library into which new data types may be plugged in as required without change to the basic deductive engine. It is shown that the operation of an existing knowledge representation system, CLASSIC, may be implemented through one data type supporting sets with upper and lower set and cardinality bounds. The architecture developed is cleanly layered by complexity of inference so that fast propagation of constraints is separated from potentially slow model checking search. Client programs may obtain estimates of the complexity of a request, and may control the resources allocated to its complete solution.