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Artificial Intelligence

Notes Knowledge-based System Support Environment

At the third level of the hierarchy is the equivalent of the Application Programming Support
Environment (APSE) in conventional systems, with facilities for editing, displaying, debugging,
and validating the knowledge base.
Knowledge-based System Shell

At the fourth level of the hierarchy is the knowledge-based system shell as a run-time
environment that elicits problem-specific information from the user, provides advice based on
its knowledge base, and explains that advice in as much detail as required.

Shell Development Language

At the fifth level of the hierarchy is the language in which the knowledge-based system shell is
written, generally a special-purpose environment for coping with knowledge representation
and inference.
Implementation Language

At the sixth level of the hierarchy is the implementation language which actually interfaces
to the computer. This tended to be Lisp in the early days of KBS, but as speed and space
efficiency have become significant and knowledge representation has become better understood,
other languages that support dynamic data structures such as C and Pascal have become
widely used.
Operating System

At the seventh level of the hierarchy is the operating system within which the implementation
runs. This needs to provide good interfaces to other programs, large databases and
communications.

Machine Architecture

At the lowest level of the hierarchy is the machine on which the KBS runs. In theory, system
developers should not need to know about the lower levels of the hierarchy machine architectures,
operating systems, and implementation languages are remote from knowledge processing. In
practice, these lower levels are the foundations on which systems are built, and any defects in
them can undermine the functionality of the upper levels.

5.1.4 Trends in Knowledge Engineering

The basic model for knowledge engineering has been that the knowledge engineer mediates
between the expert and knowledge base, eliciting knowledge from the expert, modeling and
encoding it for the knowledge base, and refining it in collaboration with the expert to achieve
acceptable performance. Figure 5.1 shows this basic model with manual acquisition of knowledge
from an expert followed by interactive application of the knowledge with multiple clients
through an expert system shell.

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