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21.4 Maintenance of Knowledge
Based Systems
Maintenance of knowledge based systems has not been given much priority
during the last two decades of historical development in AI. The need for
maintenance of expert systems is recently felt, as many of the commercial
expert systems nowadays require updating of their knowledge bases on a
regular basis. It may be noted that an augmentation of the knowledge base
causes a significant decrease in inferential throughput and thus efficiency of
the system. Designing the knowledge base in a structured manner and
partitioning it into smaller modules thus help improving the efficiency of an
expert system. An obvious question naturally arises: can we partition the
knowledge base arbitrarily? The answer to this is obviously in the negative.
The knowledge base is generally partitioned in a manner such that the related
rules belong to a common partition.
The issues to be addressed in this section include i) effects of
knowledge representation on maintainability, ii) difficulty of maintenance of
systems built with multiple knowledge engineers, iii) difficulty in maintaining
the data and control flow in reasoning.
21.4.1 Effects of Knowledge Representation
on Maintainability
Production systems have some inherent advantages of knowledge
representation. First of all, it is the simplest way to encode knowledge and
thus the experts themselves can play the role of knowledge engineers by
directly coding their expertise. This reduces the scope of errors in semantic
translation of ‘human expertise’ into machine intelligence. This idea, in fact,
motivated expert system developers to use production systems. It has been
noted recently that typical production systems offer no resistance to the entry
of inconsistency to the knowledge base. Thus for maintenance of knowledge
in production systems, we require to ‘verify’ the knowledge base to reduce the
scope of inconsistency. This is explained in fig. 21.12.
The knowledge acquisition system in fig. 21.12 generates new pieces of
knowledge, which are subsequently added to the existing knowledge base.
The knowledge base is now verified to check the existence of contradiction or
incompleteness in it. If methods for eliminating inconsistency and
incompleteness are known, that must be executed to overcome this problem.
Lee and O’Keefe [2] recently made experiments to study the effect of
knowledge representation on the maintainability of expert systems. They
observed that the time required to update knowledge by production systems