CSCI3406 Fuzzy Logic and Knowledge Based Systems AI

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CSCI3406 Fuzzy Logic and Knowledge Based Systems
(AI)
Inferencing I

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

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Introduction

• Inferencing is often linked to reasoning.
• It is the process of deriving answers of
  problems from knowledge that is maintained in a
  KBS.
• There are different types of Inferencing.
• In this lecture and next we cover some of the
  most popular and commonly used types of
  Inferencing.

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Topics of Discussion

• Basic Components
• Backward chaining.

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Basic Components

• The knowledge base
• The knowledge base contains the know-how of the
  human experts in a particular field. Such
  know-how is of two types-
• Facts - "deep knowledge".
• Heuristics - "surface knowledge".  
• Knowledge base is derived and implemented from
  our knowledge analysis and representation. How
  can a knowledge base be developed in CLIPS?

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Basic Components

• The inference engine
• The inference engine, which has two primary
  tasks
• Inference the inference engine employs reasoning
  to examine existing facts and rules. Updates
  stored knowledge and draws conclusions.
• Control the inference engine's mechanism for
  controlling the search of the knowledge base.

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Basic Components

• Working memory
• An area of the Computer's RAM reserved for
  storing information regarding the current status
  of the problem e.g. conclusions reached so far,
  data input by the user and details of items
  within the knowledge base which have been
  checked.  
• How can you monitor the working memory in Fuzzy
  CLIPS? How many parts are the working memory in
  Fuzzy CLIPS?

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Basic Components

• External data source
• Some expert systems receive input data from
  external sources other than the user. Common
  forms of this data - known as sensor data -
  include X rays, audible sounds and visual images.
  The system interprets the data and makes
  inferences based on that data.

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Backward chaining

• Backward chaining is a goal-driven approach.
• We start from an expectation of what is to
  happen (hypothesis), then seek evidence that
  supports (or contradicts) your expectation.  
• Often this entails formulating and
  testing intermediate hypotheses (or
  sub-hypotheses).
• Backward chaining may use either depth first and
  breadth first search algorithms. How?

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Backward chaining

• Example
• IF
• Customer wants comfort and Customer has
  enough_money
• THEN
• Salesperson recommends Rolls Royce
•  And assume you are the sales person, and your
  question is do I recommend Rolls Royce to this
  customer or not?

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Conclusion

• In this lecture we looked at the second aspect of
  knowledge based systems, namely, Inferencing.
• There are different types of Inferencing one of
  them is back chaining.
• Search algorithms are often used in Inferencing.

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References

• Lecture notes part 2.
• E. Turban, Expert Systems and Applied Artificial
  Intelligence. New York Macmillan Publishing
  Company, 1992.

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Next Steps

• Next
• Forward chaining.
• Other types of Inferencing.