Inferences

1
Inferences

• The Reasoning Power of Expert Systems

2
Aims

• Discuss
• strategies that can be used to guide a KBS in
  using the stored knowledge
• how the knowledge is communicated with the user
• how to make inferences from the stored knowledge

3

• Once the knowledge is acquired and stored
  (represented) the knowledge base is complete
• This must be then be processed (reasoned with)
• A computer program is required to access the
  knowledge for making inferences
• This program is an algorithm that controls a
  reasoning process
• Usually called the inference engine
• In a rule based system it is called the rule
  interpreter

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Inference Engine

• Directs the search through the KB
• Dictates which rule to fire
• Most popular techniques for searching through
  rule based systems are
• forward chaining
• backward chaining

5
Logical Process of reasoning

• information is acquired about problem - premises
• it is raining
• This is used by the logical process to create the
  output - conclusions called inferences
• IF it is raining THEN bring an umbrella
• Facts that are known to be true can be used to
  derive new facts that also must be true

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Inferencing with rules

• Rule 1
• IF it is sunny, THEN we will go to the beach
• Lets say it is sunny. This means that the premise
  (IF side) of the rule is true.
• Using a technique known as reasoning with logic
  indicates that the conclusion is also true
• We say that Rule 1 fires
• Firing a rile occurs only when all of the rules
  hypotheses (the IF parts) are satisfied (being
  either true or false)

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• When a rule is fired a conclusion is drawn and
  stored in the assertion base
• We will go to the beach is stored in the
  assertion base
• It could be use to satisfy the premise of other
  rules e.g.
• RULE 2
• If we go to the beach THEN bring bucket and spade
• The true or false values can be obtained by
• querying the user
• checking other rules

8
Inferencing with rules

• Every rule in the KB can be checked to see
  whether its premise can be satisfied by
  previously made assertions
• This process may be done in two directions
• will continue until
• no more rules can fire
• until a goal is achieved

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Chaining - Example

• Suppose you want to fly from Belfast to Lima but
  there are no direct flights
• Therefore you try to find a chain of connecting
  flights
• Two basic ways of searching
• start with all flights arriving at Lima and find
  the city where they originated
• Continue the process until you find Belfast
• because you are working backwards from your goal,
  the process is known as backward chaining

10
Forward Chaining

• List all the flights leaving Belfast and mark
  their destinations - Forward Chaining
• The search process goes through a set of
  knowledge rules. After determining which rules
  are true and which are false, the search ends
  with a finding

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Backward Chaining

• Start from a goal to be verified as either true
  or false
• Then looks for a rule that has that goal in its
  conclusion
• Then checks the premise of that rule in an
  attempt to satisfy that rule
• Checks the assertion base first. If the search
  fails there, the ES looks for another rule whose
  conclusion is the same as the premise of the
  previous rule

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Should we buy a house or not?

• RI
• IF inflation is low THEN interest rates are low
• ELSE interest rates are high
• R2
• IF interest rates are high THEN housing prices
  are high
• R3
• IF housing prices are high THEN do not buy a
  house,
• ELSE buy it

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Run a backward chaining with a high inflation
rate as given

• Starting point
• look at the rule which includes the goal in its
  conclusion - Rule3
• Step 1
• try to ascertain the outcome of the rule. At
  present all we have in the ascertain base is that
  inflation rate is high
• Step 2
• ascertain if the premise of Rule 3 is correct
• I.e. IF housing prices are high

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• Note that this premise is the conclusion of rule
  2. Therefore to verify if this premise is true or
  false we must look to the conclusion or Rule 2
• Step 3
• To ascertain the outcome of Rule 2 we must
  establish if the premise is true or false I.e.
• IF interest rates are high
• Remember, At present all we have in the asertion
  base is that inflation rate is high
• Step 4
• The premise of Rule 2 is the conclusion of Rule
  1. We have enough information in the assertion
  base too establish the outcome of Rule 1

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Backward Chaining

• Step 5
• Computer has enough information at last to
  establish that the right decision is not to buy a
  house

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Forward Chaining

• Start from the available information as it
  becomes available, then try to draw conclusions.
• These conclusions are entered into the assertion
  base
• When conclusions are drawn the computer attempts
  to solve other rules whose premises can be solved
  from information in the assertion base

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Example

• Start
• As it is known that inflation rate is high, Rule
  1 can be evaluated and the conclusion entered
  into the assertion base
• Interest rates are high
• Step 1
• The premise of Rule 2 depends on the outcome of
  Rule 1. As this is known the conclusion of Rule 2
  can be determined and entered into the assertion
  base
• Housing prices are high

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Example

• Step 3
• The premise of Rule 3 depends on the outcome of
  Rule 2. As this is known the conclusion of Rule 3
  can be determined and entered into the assertion
  base
• No not buy house
• Step 4
• As this was the original goal the chaining stops
• Another example is given in section 15.3

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The Inference Tree

• Provides a schematic view of the inference
  process (similar to a decision tree)
• Each rule is composed of a premise and a
  conclusion
• In the diagram each of these is represented by a
  node
• Branches connect the premises and conclusions
• The operators AND and OR are used to reflect the
  structure of the rules
• A form of Knowledge Representation

20
The Inference Tree

• By using the tree, we can visualise the process
  of inference and movement along the branches of
  the tree
• This is called tree traversal
• Various strategies for this
• To traverse an OR node, it is sufficient to
  traverse just one of the nodes below
• To traverse an AND node, we must traverse all the
  nodes below it

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Diagram

• Rule 1
• IF it is likely to rain THEN take an umbrella
• Rule 2
• IF it is cloudy THEN it is likely to rain
• Rule 3
• IF the forecast is bad THEN it is likely to rain

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Model-Based Reasoning

• Depends on knowledge of the structure and
  behaviour of a device, rather than relying on
  production rules that represent expertise
• Study section 15.6

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Case-based Reasoning (CBR)

• Adapt solutions that were used to solve old
  problems
• Use them to solve new problems
• Study section 15.7