Artificial intelligence and expert system

Artificial Intelligence (AI)
Effort to develop computer-based systems
that behave like humans:
learn languages
accomplish physical tasks
use a perceptual apparatus
emulate human thinking

Artificial Intelligence (AI)…………
 It is the science and engineering of making
intelligent machines, especially intelligent computer
programs.
 Artificial intelligence (AI) may be define as "the
science and engineering of making intelligent
machines.“

Applications of AI
 Artificial intelligence has successfully been used in a
wide range of fields including medical diagnosis, stock
trading, robot control, law, scientific discovery, video
games and toys.

AI Branches
 Natural Language
 Robotics
 Perceptive Systems
 Expert Systems
 Intelligent Machines

Expert System
 Expert systems are the most mature and
widely-used commercial field of artificial
intelligence.
 Expert systems are systems that employ a
codification of human expertise to solve
problems.

Expert System…………
 An expert system is a specialized computer program
that can analyze complex situations within a
particular area of expertise, reach useful conclusions
about those situations, and offer recommendations
for appropriate action.
 Expert systems are computerized tools designed to
enhance the quality and availability of knowledge
required by decision makers in a wide range of
industries. They augment conventional programs such
as databases, word processors, and spreadsheet
analysis.

Expert System…………
 An expert system is software that attempts to
reproduce the performance of one or more human
experts, most commonly in a specific problem domain,
and is a traditional application and/or subfield of
artificial intelligence.
 In an expert system, the computer applies reasoning
methodologies in a knowledge-specific domain to
render advice or make recommendations, much like a
human expert. Expert systems achieve high levels of
performance in task areas that, for human beings,
require years of special education and training.

Expert System……………
Components
 Knowledge Base
 Inference Engine
 The User Interface
 Explanation Mechanism

Components……………
 Knowledge Base
• Knowledge base is the 'nerve centre' of the expert
system. The knowledge base is a collection of rules or
other information structures derived from the human
expert. The knowledge base stores all the facts and
rules about a particular problem domain. It makes
these available to the inference engine in a form that it
can use.

 Inference Engine
• The inference engine is the main processing element
of the expert system.The inference engine is the
program that locates the appropriate knowledge in the
knowledge base, and infers new knowledge by applying
logical processing and problem-solving strategies.

 The User Interface
• The user interface is the means of communication
between a user and the expert systems problem-solving
processes.

 Explanation Mechanism
• The method by which an expert system reaches
a conclusion may not be obvious to a human user,
so many expert systems will include a method
for explaining the reasoning process that lead
to the final answer of the system.

General Category
 For Decision Support: To remind a human expert of
issues to consider, alternatives to explore, etc. that
the human expert may have missed in his decision
making. This type of Expert System is commonly found
in the area of medicine.
 For Decision Making: To aid a person in problem
solving an area that he/she is unfamiliar in, or is
inexperienced in. This type of expert system is
commonly found in industrial systems.

Advantages
 Provides consistent answers for repetitive decisions,
processes and tasks
 Holds and maintains significant levels of information
 Encourages organizations to clarify the logic of their
decision-making
 Never "forgets" to ask a question, as a human might
 Can work round the clock
 Can be used by the user more frequently
 A multi-user expert system can serve more users at a
time

Disadvantages
 Lacks common sense needed in some decision making
 Do not learn by experience
 Cannot make creative responses as human expert would
in unusual circumstances
 Domain experts not always able to explain their logic
and reasoning
 Errors may occur in the knowledge base, and lead to
wrong decisions
 Cannot adapt to changing environments, unless
knowledge base is changed

Application (General)
 Acquisition and interpretation of data:
Examples are Netrics Data Quality Software of Netrics
Inc., Nuggets by Data Mining Technologies Inc. etc.
 Diagnosis and Abnormal Condition Management:
Examples are EasyDiagnosis by Easydiagnosis, Intelligent
Objects by Gensym Inc., Transformer Oil Analyst (TOA),
developed by Delta-X Research.

Application (General)………………
 Decision-making or decision support
Examples are BlazeAdviser and Blaze Decision System
by Fair Isaak Inc., ReThink by Gensym Corporation.
 Design and management solutions
Examples: Goldfire Innovation and Goldfire Intelligence
developed by Invention machine.

Application (General)………………
 Human Resource Management Systems (HRMS)
e.g. CCHKnowledgepoint, ESI (Expert Solutions
International)
 Marketing and trade management
Examples are Campaign Optimizer, Preview
Reporting and Opportunity Mapper by PreVision
Marketing LLC, CycAnswers by Cycorp Inc.

Application in Library and Information Systems…………
 Cataloguing
There have been many applications of expert systems in
cataloguing. One is that it helps in the identification of
bibliographic elements for cataloguing from electronic
version of title pages and automatic determination of the
main entry for books. Examples of such expert systems are:
MUSic Cataloguing (MUSCAT) system which is designed to
assist in cataloguing musical pieces and the AUTOCAT,
which attempted to generate bibliographic records of
periodical literature in the physical sciences that were
available in machine-readable form.

Application in Library and Information Systems……………
 Classification
The classification of library materials using the Universal
Decimal Classification (UDC) system, Library of Congress
Classification (LC) system, Dewey Decimal Classification
(DDC) system or others is a fundamental function for
libraries which has traditionally been performed by experts
in classification. The N-Cube expert system is an example
of using expert system technology to assist in the
classification of library materials using the UDC.

 Indexing and Abstracting
Another advantage of expert system is automatic
indexing of keywords and automatic abstracting.
Different methods are automatic indexing by extraction,
automatic organization of terms or documents into
classes, automatic approach to thesaurus construction,
matching natural language requests against the text of
document etc. Example of such expert system is the
Structured Information Management: Processing and
Retrieval (SIMPR).

 Collection Development and Acquisition
The economic crisis related to the acquisition of library
materials has increased the importance of selecting and
acquiring the best and most relevant materials within
budgetary constraints. Expert systems have been
developed to select materials in very specific domains.
One of the examples of such expert system is Selection
Advisor which assists in collection development at
academic libraries. Expert system can also be used in the
automatic selection of journals.

 Reference Services
Expert systems are well suited to reference services.
Consequently, most of the research and development
pertaining to expert systems in libraries has been in the
areas of reference and retrieval. Such a reference
systems would be able to provide a self help reference
environment to all times. A number of such systems have
been developed such as PLEXUS, AQUAREF, RESDA etc.

 Information Search and Retrieval
Studies on OPAC use have revealed that effective use of
on-line retrieval is difficult to achieve for novices as well
as experts. A major problem area is the match between a
user's search terms and correct subject terms. An expert
system can understand the users needs, intentions and
goals and provide aid in on line retrieval. An expert system
can also deal with the problem of term selection.
Examples: CANSEARCH, LOOK etc.

Artificial intelligence and expert system

Artificial intelligence and expert system

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