CSCI3406 Fuzzy Logic and Knowledge Based Systems AI

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

• Inferencing is the 3rd stage of a KBS design and
  often linked to reasoning
• Inferencing is the process of deriving correct
  appropriate answers and drawing correct
  conclusions for problems from knowledge that is
  maintained in a KBS
• There are different types of Inferencing
• In this lecture we cover some of the most popular
  and commonly used types of Inferencing

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Topics of Discussion

• Basic Components
• Backward chaining
• Forward chaining
• Opportunistic Inferencing
• Logical Inferencing
• Closed World Assumption (CWA)
• Logical contradiction
• Other techniques

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Basic Components

• An Expert System can be described as
• Expert system
• inference engine
• knowledge base
• working memory
• (external) data

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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 by
  means of knowledge acquisition and representation.

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Basic Components The knowledge base Heuristics

• Heuristic - a strategy to find the solution to a
  problem which is not guaranteed to work.
• One sort of heuristic usually gives you the right
  answer but sometimes gives you the wrong answer
• Another sort gives you an answer which isnt 100
  accurate.

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Basic Components The knowledge base Heuristics

• Humans use heuristics a great deal in their
  problem solving. Of course, if the heuristic does
  fail, it is necessary for the problem solver to
  either pick another heuristic, or know that it is
  appropriate to give up.
• The rules, found in the knowledge bases of
  rule-based systems, are very often heuristics.

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Basic Components The inference engine

• The inference engine has two primary tasks
• Inference the inference engine employs reasoning
  to examine existing facts and rules. It also
  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.

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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 some biomedical test results (e.g., red
  cell count in blood), audible sounds and visual
  images. The system interprets the data and makes
  inferences based on that data.

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Inference in a KBS

• Two control strategies
• forward chaining
• backward chaining

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Backward chaining

• Backward chaining from the conclusion to the
  facts. Sometimes called a goal-driven approach
• To chain backward, match a goal in working memory
  against 'conclusions' of rules in the rule-base.
  We start from an expectation of what is to happen
  (hypothesis), then seek evidence that
  supports (or contradicts) your expectation.
• When one of them fires, this is liable to produce
  more goals.
• So the cycle continues.
• Often this entails formulating and
  testing intermediate hypotheses (or
  sub-hypotheses).
• Backward chaining may use either depth first and
  breadth first search algorithms

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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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Forward chaining

• Forward chaining working from the facts to a
  conclusion. Sometimes called a data-driven approac
  h.
• In this approach we start from available
  information as it comes in, or from a basic idea,
  then try to draw conclusions.
• The computer analyses the problem by looking for
  the facts that match the IF portion of its
  IF-THEN rules.

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Forward chaining

• If we take the sales person example again,
  forward chaining would be the approach to use if
  the question was the customer wants comfort and
  has enough money, what shall I recommend to him?

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Backward and Forward chaining

• Here are two rules
• If corn is grown on poor soil, then it will rot.
• If soil hasn't enough nitrogen, then it is poor
  soil.
• Forward chaining This soil is low in nitrogen
  therefore this is poor soil therefore corn grown
  on it will rot.
• Backward chaining This corn is rotten therefore
  it must have been grown on poor soil therefore
  the soil must be low in nitrogen.

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Forward chaining

• More realistically,
• the forward chaining reasoning would be there's
  something wrong with this corn. So I test the
  soil. It turns out to be low in nitrogen. If
  thats the case, corn grown on it will rot.
  Therefore the problem is rot caused by low
  nitrogen.

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Backward chaining

• More realistically,
• the backward chaining reasoning would be there's
  something wrong with this corn. Perhaps it is
  rotten if so, it must have been grown on poor
  soil if so, the soil must be low in nitrogen. So
  test for low nitrogen content in soil, and then
  we'll know whether the problem is rot.

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Forward backward chaining

• The choice of strategy depends on the nature of
  the problem.
• Assume the problem is to get from facts to a goal
  (e.g. symptoms to a diagnosis).

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Forward backward chaining

• Backward chaining is the best choice if
• The goal is given in the problem statement, or
  can sensibly be guessed at the beginning of the
  consultation
• or
• The system has been built so that it sometimes
  asks for pieces of data (e.g. "please now do the
  gram test on the patient's blood, and tell me the
  result"), rather than expecting all the facts to
  be presented to it.

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Forward backward chaining

• Backward chaining
• This is because (especially in the medical
  domain) the test may be
• expensive,
• or unpleasant,
• or dangerous for the human participant
• so one would want to avoid doing such a test
  unless there was a good reason for it.

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Forward backward chaining

• Forward chaining is the best choice if
• All the facts are provided with the problem
  statement
• or
• There are many possible goals, and a smaller
  number of patterns of data
• or
• There isn't any sensible way to guess what the
  goal is at the beginning of the consultation.

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Forward backward chaining

• Note also that
• a backwards-chaining system tends to produce a
  sequence of questions which seems focussed and
  logical to the user,
• a forward-chaining system tends to produce a
  sequence which seems random unconnected.
• If it is important that the system should seem to
  behave like a human expert, backward chaining is
  probably the best choice.

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Forward backward chaining

• Some systems use mixed chaining, where some of
  the rules are specifically used for chaining
  forwards, and others for chaining backwards. The
  strategy is for the system to chain in one
  direction, then switch to the other direction, so
  that
• the diagnosis is found with maximum efficiency
• the system's behaviour is perceived as "human".

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Backward and Forward

• Both backward and forward chaining techniques
  have their advantages and disadvantages and are
  often used in permutations.
• Compare advantages and disadvantages of both
  techniques.
• When this happens, a technique that is called
  opportunistic Inferencing is used.

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Opportunistic Inferencing

• The idea behind opportunistic Inferencing is that
  some asserted facts in the working memory might
  lead to the firing of set of rules, which leads
  to speed up the Inferencing process.
• The Inferencing engine in FUZZYCLIPS can be used

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Logical Inferencing

• Logical Inferencing, which is sometimes referred
  to as problem solver or automated proof system,
  uses the logical representation to derive or
  entail conclusions.
• Logic Inferencing is usually computationally
  complicated. Therefore, several assumptions are
  used in logic Inferencing.
• The most famous assumption that is often used in
  logic-based models and systems is Closed World
  Assumption (CWA).

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Closed World Assumption (CWA)

• CWA assumes that the logical system is closed and
  therefore there are no possible external effects
  to change the composition of this system.
• As a result anything that cannot be proved to be
  true in the model we can assume that it is be
  false.
• This sometimes leads to difficulties, for an
  example, missing symptoms of an illness does not
  necessary that the patient does not have that
  illness. However, that is exactly what the system
  may conclude using the closed world assumption.

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More Logical Inferencing

• There are several logic-based models e.g.
  default logic, non-monotonic logic and situation
  calculus.
• The logic-models are often very complicated to
  be translated into computable models.
• We always seek that our models are sound and
  complete.
• What do you think we mean by soundness and
  completeness?

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Logical contradiction

• One of the weaknesses, beside complexity, is that
  the logic-based systems suffer from is usually
  Logical Contradiction.
• Logical contradiction happens when the system,
  for example, allows us to entail X and not X at
  the same time. Sometimes logical contradiction is
  defined within the context of KBS to be the case
  in which the system reaches a conclusion that is
  known by the user to be false.

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Other Inferencing techniques

• There are several Inferencing techniques may be
  used depending on the system domain. Here are
  some of the commonly used techniques

• Model-based reasoning.
• Case-based reasoning.
• Monotonic and non-monotonic reasoning.
• Truth maintenance systems.

• Hypothetical reasoning.
• Analogical reasoning.
• Procedural reasoning.

• Hierarchical reasoning.
• Inductive and deductive reasoning.
• Pattern matching.

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Conclusion

• In this lecture we looked at the 3rd aspect of
  knowledge based systems, namely, Inferencing.
• There are different types of Inferencing (e.g.,
  backward forward chaining/ inferencing and
  logical inferencing)
• 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
• Modelling uncertainty