INGENIEUR
Table 1: Differences between Conventional Systems and Expert Systems (Negnevitsky, 2005)
Conventional Systems Expert Systems
Information and processing combined in
a single sequential programme The knowledge base is separated from
the inference engine
The programme is never wrong The programme could make a mistake.
Complete input data is required. Incomplete input data is acceptable.
Changes of programme are time
consuming. Changes of programme can be made
with ease.
Quantitative data Qualitative data
Numerical data representation Symbolic data representation.
level problem-solving, expert systems generally:
(Negnevitsky, 2005).
1. Support inspection of their reasoning
processes, both in presenting intermediate
steps and in answering questions about
the solution process.
2. Simplify and ease modification which
enables adding and deleting skills from
the knowledge base.
3. Reason heuristically, using knowledge to
obtain useful problem solutions.
The reasoning of an expert system is open to
inspection to provide useful information about
the state of art and to explain the choices and
decisions made by the programme. Explanations
are important in expert systems for many reasons.
Doctors or engineers who seek recommendations
from an expert system must be satisfied with
the solution. The explanations help people relate
the advice with their existing understanding of
the domain and apply it in a more confident and
flexible manner. The major application area of an
expert system includes (but is not limited to) the
following: interpretation, prediction, diagnosis,
planning design control and instruction. (Shu,
2005). Table 1 shows the differences between
conventional systems and expert systems.
Expert systems are being applied to a variety of
problem domains to assist in the decision making
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process (Chen et al., 2012). It is able to provide
solutions for different problems in the industry
ranging from planning to marketing strategies
(Li, 2005) to re-engineering consultation (Hvam
et al., 2004; Ruiz et al., 2011). Nowadays, expert
systems are applied in various fields with different
applications, such as:
● ●
Constructing a nutrition diagnosis expert
system (Chen et al., 2012)
● ●
An expert system for diagnosing problems
in boiler operations (Artificial et al., 1992)
● ●
An expert system hybrid architecture
to support experiment management
(Fiannaca et al., 2014)
● ●
A knowledge-based expert system for
selection of appropriate structural systems
for large spans (Golabchi, 2008)
● ●
Expert system for making durable concrete
for aggressive carbon-dioxide exposure
(Islam & Miah, 2011)
● ●
Expert system for Sri Lankan solid waste
composting (Jayawardhana et al., 2003)
● ●
A prototype rule-based expert system for
travel demand management (Mansyur et
al., 2013)
● ●
Development of an expert system for the
design of airborne equipment (Kumar et al.,
2004)
● ●
An expert system development tool for non-
AI experts (Ruiz et al., 2011)