Knowledge representation is perhaps the most central problem
confronting artificial intelligence. Expert systems need knowledge
of their domain of expertise in order to function properly.
Computer vlslOn systems need to know characteristics of what they
are "seeing" in order to be able to fully interpret scenes. Natural
language systems are invaluably aided by knowledge of the subject
of the natural language discourse and knowledge of the participants
in the discourse. Knowledge can guide learning systems towards
better understanding and can aid problem solving systems in
creating plans to solve various problems. Applications such as
intelligent tutoring. computer-aided VLSI design. game playing.
automatic programming. medical reasoning. diagnosis in various
domains. and speech recogOltlOn. to name a few. are all currently
experimenting with knowledge-based approaches. The problem of
knowledge representation breaks down into several subsidiary
problems including what knowledge to represent in a particular
application. how to extract or create that knowledge. how to
represent the knowledge efficiently and effectively. how to
implement the knowledge representation scheme chosen. how to modify
the knowledge in the face of a changing world. how to reason with
the knowledge. and how tc use the knowledge appropriately in the
creation of the application solution. This volume contains an
elaboration of many of these basic issues from a variety of
perspectives.
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