Inferences
The Reasoning Power of Expert
Systems
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
• 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
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
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
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)
• 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
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
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
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
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
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
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
– 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
Backward Chaining
• Step 5
– Computer has enough information at last to
establish that the right decision is not to buy a
house
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
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
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
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
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
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
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
Case-based Reasoning (CBR)
• Adapt solutions that were used to solve old
problems
• Use them to solve new problems
• Study section 15.7