IMAT3406 Fuzzy Logic and Knowledge Based Systems (AI)

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IMAT3406 Fuzzy Logic and Knowledge Based
Systems (AI)
Introduction to Knowledge Based Systems (KBS)
Most of the KBS notes kindly provided by Dr.
Aladdin Ayesh
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Lecture Plan for Knowledge Based System
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Reading ListNot compulsory, but complementary

• Knowledge Based Systems
• E. Turban, Expert Systems and Applied Artificial
  Intelligence. New York Macmillan Publishing
  Company, 1992.
• T. Dean, J. Allen, and Y. Aloimonos, Artificial
  Intelligence Theory and Practice The
  Benjamin/Cummings Publishing Company, Inc., 1995.
• P. Jackson, Introduction to Expert Systems,
  Second Edition ed. UK Addison Wesley Publishing
  Company, 1990.

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Introduction

• In this lecture, we cover an introduction to KBS.
• We start with identifying the different types of
  AI numerical and symbolic.
• We look at some search algorithms as simple AI
  system.

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Topics of Discussion

• AI
• Simple AI systems
• Developing KBS
• Some famous KBS

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AI

• Artificial Intelligence is the field of computing
  that attempts at providing computational models
  of some human activities, which researchers
  consider intelligent activities, such as
  learning, acting, decision making, evolving and
  so on. AI, therefore, relates strongly to fields
  such as psychology, biology and sociology. In
  some cases new disciplines emerged such as
  bio-informatics and cybernetics.

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AI

• There are two main streams in developing AI
  systems quantitive and qualitative approaches.
• Quantitive approaches sometimes referred to as
  numerical approaches, because they use quantities
  in analysing the problems.
• Neural nets, fuzzy logic, genetic algorithms are
  all examples of the quantitive approach.

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AI

• Qualitative approaches sometimes referred to as
  symbolic approaches, because they use qualities
  of the problem to solve the problem.
• Logic, rules, lists based systems are examples of
  qualitative AI systems.

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Simple AI systems

• The simplest view of AI systems is as a search
  problem solver. It is almost impossible to
  develop an expert system without implementing
  some search technique or another to navigate
  through the problem domain for the solution.
  Search techniques provide the base for the
  inference engine, which is an essential component
  of any expert system.

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Simple AI systems

• There are two main types of searches
  Conventional searches and heuristic searches.
• Conventional searches cover the entire domain and
  eventually find the solution, what is the problem
  with that?
• Heuristic searches aim at reducing the domain or
  covering a selected portion of the problem
  domain. What is the problem with that?

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Simple AI systems

• Conventional searches include
• Depth first search
• Breadth first search
• Heuristic searches include
• Generate and test.
• Hill climbing.
• Best first.
• Problem reduction.
• Constraint satisfaction.
• Means-end analysis.

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Developing KBS

• (Please refer to the second lecture and lecture
  notes part 2)
• Many KBSs are symbolic systems.
• There are two distinctive parts need to be
  included in any KBS
• Knowledge representation, which is usually the
  result of knowledge acquisition
• Inference Engine, which you would not usually
  need to develop if you are using an expert system
  shell such as CLIPS

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Developing KBS

• In KBS, we also call them exact systems, we do
  not need to imply certainty factor as we did in
  FLS.
• In CLIPS, KBS can be developed as pure rules
  without the need to define fuzzy sets, i.e. no
  deftemplate is required.

CLIPS is a productive development and delivery
expert system tool which provides a complete
environment for the construction of rule and/or
object based expert systems., CLIPS was created
in 1985 and is now widely used throughout the
government, industry, and academia. For further
details including its key features, please see
http//www.ghg.net/clips/WhatIsCLIPS.html
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Some famous KBS

• DENDRAL (Late 60s)
• MYCIN (Mid 1970s)
• R1/XCON (1980s)

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DENDRAL (1965-83)

• DENDRAL (1965-83) The DENDRAL Project was one of
  the earliest expert systems. DENDRAL began as an
  effort to explore the mechanization of scientific
  reasoning and the formalization of scientific
  knowledge by working within a specific domain of
  science, organic chemistry. Another concern was
  to use AI methodology to understand better some
  fundamental questions in the philosophy of
  science, including the process by which
  explanatory hypotheses are discovered or judged
  adequate. After more than a decade of
  collaboration among chemists, geneticists, and
  computer scientists, DENDRAL had become not only
  a successful demonstration of the power of
  rule-based expert systems but also a significant
  tool for molecular structure analysis, in use in
  both academic and industrial research labs. Using
  a plan-generate-test search paradigm and data
  from mass spectrometry and other sources, DENDRAL
  proposes plausible candidate structures for new
  or unknown chemical compounds. Its performance
  rivals that of human experts for certain classes
  of organic compounds and has resulted in a number
  of papers that were published in the chemical
  literature. Although no longer a topic of
  academic research, the most recent version of the
  interactive structure generator, GENOA, has been
  licensed by Stanford University for commercial
  use.
• (taken from http//smi-web.stanford.edu/projects/h
  istory.html)

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MYCIN (1972-80)
MYCIN is an interactive program that diagnoses
certain infectious diseases, prescribes
antimicrobial therapy, and can explain its
reasoning in detail. In a controlled test, its
performance equalled that of specialists. In
addition, the MYCIN program incorporated several
important AI developments. MYCIN extended the
notion that the knowledge base should be separate
from the inference engine, and its rule-based
inference engine was built on a
backward-chaining, or goal-directed, control
strategy. Since it was designed as a consultant
for physicians, MYCIN was given the ability to
explain both its line of reasoning and its
knowledge. Because of the rapid pace of
developments in medicine, the knowledge base was
designed for easy augmentation. And because
medical diagnosis often involves a degree of
uncertainty, MYCIN's rules incorporated certainty
factors to indicate the importance (i.e.,
likelihood and risk) of a conclusion. Although
MYCIN was never used routinely by physicians, it
has substantially influenced other AI research.
At the HPP, MYCIN led to work in TEIRESIAS,
EMYCIN, PUFF, CENTAUR, VM, GUIDON, and SACON, all
described below, and to ONCOCIN and ROGET. The
book Rule-Based Expert Sytem The MYCIN
Experiment at the Stanford Heuristic Programming
Project describes the decade of research on MYCIN
and its descendants. (taken from
http//smi-web.stanford.edu/projects/history.html)
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R1/XCON (1980s)

• One of the first commercially successful expert
  systems
• Application domain
• configuration of minicomputer systems
• selection of components
• arrangement of components into modules and cases
• Approach
• data-driven, forward chaining
• consists of about 10,000 rules written in OPS5
• Results
• quality of solutions similar to or better than
  human experts
• roughly ten times faster (2 vs. 25 minutes)
• estimated savings 25 million/year

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Conclusion

• AI systems and search algorithms.
• Developing KBS.

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Next Steps

• Next
• Knowledge acquisition.