Reasoning System
Reasoning System
Reasoning with rules
Forward chaining
Backward chaining
Rule examples
Fuzzy rule systems
Planning
Reasoning with rules
 Rule format: If-then-else rules
 The reasons for using rules
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Easily understand
Easily read
Easily add
Easily modify
Reasoning with rules
 Rules are normally manipulated by reasoning
systems:
– Forward chaining: can generate new facts.
– Backward chaining: can deduce whether statements
are true or not.
Rule base system
 An expert system or knowledge-based system is
the common term used to describe a rule-based
processing system. It consists three elements:
– A knowledge base (the set of if-then-else rules and
known facts)
– A working memory or database of derived facts and
data
– An inference engine which contains the reasoning
logic used to process the rules and data.
An example of simple rule
if num_wheel =4 and motor=yes
then vehicle =automobile
Antecedent Clause
Consequent Clause
An example of simple rule
A rule states a relationship between clauses
(assertions or facts) and, depending on the situation,
can be used to generate new information or prove
the truth of an assertion.
A conclusion generated by the reasoning rule
can be add that fact to our knowledge base.
On the other hand, if we are trying to prove a
fact is true( rhs), we need to find out the antecedent
clauses have been presented (lhs).
Reasoning Engine
 Forward reasoning:
– Using facts and rules to derive new facts
 Backward reasoning:
– Trying to prove an assertion in the consequence of a
rule by showing that the antecedent clauses are
true.facts and rules to derive new facts
Triggered/Ready to Fired
 A rule whose antecedent clauses are all true is
said to be triggered or ready to fired.
 We fire a triggered rule by asserting the
consequence clause and adding it as a fact to our
working memory.
 At any time, a rule base may contain several rules
that are ready to fire. It is up to the control
strategy of the inference engine to decide which
one gets fired.
Identifying the Rules
 Most rule-based system allow rules to have
names or labels such as Rule 1: or Automobile:
to easily identify rules for editing or for tracing
during inferencing.
Rule representation
 Some systems allow disjunctions (or) between
antecedent clauses. This is a short-hand that
reduces the size of a rule base.
– Rule 1: if num_wheel =2 then vehicleTYpe = cycle
– Rule 2: if num_wheel =3 then vehicleTYpe = cycle
– Rule 3: if (num_wheel =2 or num_wheel =3 ) then
vehicleTYpe = cycle
 Rule 3 could replace Rule 1 and Rule 2 in the
rule base.
Rule representation
 Most rule systems also allow boolean condition
operators such as <, >, and !=, in addition to
equality.
– Rule 4: if (num_wheel >1) and (num_wheel
<4 ) then vehicleTYpe = cycle
Rule representation
confidence or certainty factor
 Another common enhancement to rule syntax is
the addition of a confidence or certainty factor.
 If we only write rules which applied 100 percent
of the time with 100 percent confidence, we
would have a very small knowledge base.
 If we have little confidence in our predictive
antecedent clauses, we may lower the certainty
factor of the rule to 50 percent.
Rule representation
confidence or certainty factor
 The certainty factor are commonly given by
heuristics or rules of thumb.
 Example of rule with certainty factor:
– Rule 5: if (whether_forcast= rain) and
(whether_probability >80%) then
(chance_of_rain =high) with CF:90
Rule representation
- Sensor
 Many rule-based system allows functions to be
called from the antecedent clause., These
functions are called sensors, because they go out
of the inference engine and test some condition in
the environment.
 Sensors usually return boolean values, but they
may also return data or facts to the working
memory.
Rule representation
- Effector
 When functions are allowed in the consequent,
they are called effectors, which greatly expand
the capability of the rule-based system.
 An effector returns a rule from a fact-generating
mechanism into an action-generating device.
These action rules allow intelligent agents to do
things for us.
Example of Sensor and Effector
action
 An intelligent agent that processes e-mail might
contain the following rule:
– Rule 6: if sensor(mailArrived) then
effector(processMail)
– where mailArrived and processMail are
defined as functions which interface with the
e-mail system, and sensor() and effector() are
methods provided by the inferencing system
for invoking those functions.
Rulebase Management
 In small numbers, rule can adequately represent
many types of domain knowledge. However, as
the number of rules grows, the intuitive aspects
of if-then rules are diminished, and they lose their
effectiveness from a readability perspective.
 Commercial rule-base systems often allow
grouping or partitioning of rules so that they can
be treated as logical blocks of knowledge in an
attempt to overcome this weakness.
Rulebase Management
 As more complete information comes in, or as
things change in the outside world, rules which
may have been true before become false. The
consequence is that we may have to “take back”
some of the “facts” which were generated by the
rules.
– For example, if we see that the grass is wet, we may
fire a rule that concludes it is raining. However, we
may then get information that the sprinkler system is
on. This may cause us to retract our assertion that it is
raining or at least lower our confidence or certainty in
making that conclusion.
Rulebase Management
 Dealing with changes and retracting facts or
assertions is called nonmonotonic reasoning.
 Keeping a rule-base consistence by managing
dependencies between inferred facts requires a
truth maintenance system.
 Most reasoning systems, such as predicate logic,
are monotonic, that is, they add information but
do nor retract information from the knowledge
base.