Knowledge representation declarative

One of the examples is KRL, developed by Bobrow and Winograd from Stanford University [10]. KRL supports knowledge representation in different declarative forms based on conceptual objects that form a network. KRL is basically an interpreter that allows LISP expressions to be formulated in a more expressive fashion. It includes an inference engine with a backward-chaining mechanism. 

Knowledge machine (KM) is a frame-based knowledge representation langnage similar to KRL and other KL-ONE representation languages such as Loom and CLASSIC [13-15], In KM, a frame denotes either a class (i.e., type) or an instance (i.e., individual). Frames have slots, or binary predicates, in which the fillers are axioms about the slot s value. These axioms have both declarative and procedural semantics, allowing for procedural inference. 

Knowledgebase oriented Ill-structured problem representation, logic (declarative) programming Procedural execution Knowledge rep esentation handling, inference engine with procedural attachment, e.g., DICEtalk with percept knowledge representation... 

The domain module represents what is being teached and contains usually a set of facts and/or rules representing the knowledge we have about the domain. Sometimes this part of the system is also responsible for the automatic generation of examples. A lot of attention has been paid to the problem of domain knowledge representation, the solutions ranging from declarative (frame-like) to procedural ones (production rules). 

In the previous section, we presented the modeling classes that allow one to describe the structure and behavior of chemical processing systems. Such a representation provides and structures the declarative knowledge about the current state of the design. 

---