CSCI3406 Fuzzy Logic and Knowledge Based Systems AI

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CSCI3406 Fuzzy Logic and Knowledge Based Systems
(AI)
Introduction to KBS

• Aladdin Ayesh
• http//www.cse.dmu.ac.uk/aayesh/

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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 sometime 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.

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Some famous KBS

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

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Conclusion

• AI systems and search algorithms.
• Developing KBS.

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References

• Lecture notes part 2.

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Next Steps

• Next
• Knowledge acquisition.