Forward Backward Chaining

FORWARD AND BACKWARD
CHAINING
PREPARED BY: QURAT UL AIN

DEFINITION
• Forward chaining is a data driven method of
deriving a particular goal from a given
knowledge base and set of inference rules
• Inference rules are applied by matching facts
to the antecedents of consequence relations
in the knowledge base
• The application of inference rules results in
new knowledge (from the consequents of the
relations matched), which is then added to
the knowledge base

FORWARD CHAINING (Ben Coppin)
In Forward chaining, the system starts from a set
of facts, and a set of
rules, and tries to find a way of using those rules
and facts to deduce a conclusion or come up
with a suitable course of action.
This is known as data-driven reasoning because
the reasoning starts from
a set of data and ends up at the goal, which is
the conclusion.

Forward Chaining Mechanism
(Ben Coppin)
When applying forward chaining, the first step is to take
the facts in the fact database and see if any combination
of these matches all the antecedents of one of the rules
in the rule database. When all the antecedents of a rule
are matched by facts in the database, then this rule is
triggered.
Usually, when a rule is triggered, it is then fired, which
means its conclusion is added to the facts database. If
the conclusion of the rule that has fired is an action or a
recommendation, then the system may cause that action
to take place or the recommendation to be made.

FORWARD CHAINING EXAMPLE
Ben Coppin

Rule 4
IF on second floor
AND button is pressed on first floor
AND already going to third floor
THEN remember to go to first floor later
Let us imagine that we start with the following facts in our
database:
Fact 1
At first floor
Fact 2
Button pressed on third floor
Fact 3
Today is Tuesday

Now the system examines the rules and finds that
Facts 1 and 2 match the antecedents of Rule 3.
Hence, Rule 3 fires, and its conclusion “Go to
third floor” is added to the database of facts.
Presumably, this results in the elevator heading
toward the third floor. Note that Fact 3 was
ignored altogether because it did not match the
antecedents of any of the rules.
Now let us imagine that the elevator is on its way to
the third floor and has reached the second
floor,when the button is pressed on the first floor.
The fact “Button pressed on first floor” Is now
added to the database, which results in Rule 4
firing.

Now let us imagine
that later in the day the facts database contains the
following information:
Fact 1
At first floor
Fact 2
Button pressed on second floor
Fact 3
Button pressed on third floor
In this case, two rules are triggered—Rules 2 and 3.
In such cases where there is more than one
possible conclusion, conflict resolution needs to
be applied to decide which rule to fire.

Forward chaining applies a set of rules and facts
to deduce whatever conclusions can be
derived, which is useful when a set of facts are
present, but you do not know what
conclusions you are trying to prove.
In some cases,forward chaining can be
inefficient because it may end up proving a
number of conclusions that are not currently
interesting. In such cases, where a single
specific conclusion is to be proved, backward
chaining is more appropriate.

FORWARD CHAINING
(ANOTHER EXAMPLE)
“WEATHER FORECASTING SYSTEM”

• Suppose we have been given the following
facts,
a) Sun is behind the clouds.
b) Air is very heavy and cool.
• Problem: Using Forward chaining try to
conclude that there are chances of rain.

CONFLICT RESOLUTION (Ben Coppin)
In a situation where more than one conclusion
can be deduced from a set of facts, there are a
number of possible ways to decide which rule
to fire (i.e., which conclusion to use or which
course of action to take).

CONFLICT RESOLUTION
• Example:
IF you’re bored
AND you’ve no cash
THEN go to a friend’s place.
IF you’re bored
AND You’ve no cash
THEN go to a park.

CONFLICT RESOLUTION STRATEGIES
• We have different resolution strategies:
1) Fire the first rule in sequence.
2) Assign rule priorities (by importance).
3) More specific rules are preferred over more
general rules.(e.g. a rule having 5 IF’s(handle more info)
will be preferred over one having 3 IF’s)
4) Prefer rules whose premises are added more
recently (time stamping)
5) Parallel strategy (create view points)

CONFLICT RESOLUTION
TECHNIQUES
BEN COPPIN

• An alternative method is the longest-matching strategy. This
method involves firing the conclusion that was derived from
the longest rule.
• For example:
IF patient has pain
THEN prescribe painkiller
IF patient has chest pain
AND patient is over 60
AND patient has history of heart conditions
THEN take to emergency room
Here, if all the antecedents of the second rule match, then
this rule’s conclusion should be fired rather than the
conclusion of the first rule because it is a more specific match.

A further method for conflict resolution is to fire
the rule that has matched the facts most
recently added to the database. In each case,
it may be that the system fires one rule and
then stops (as in medical diagnosis), but in
many cases, the system simply needs to
choose a suitable ordering for the rules (as
when controlling an elevator) because each
rule that matches the facts needs to be fired
at some point.

DEFINITION
• Backward chaining is a goal driven method of
deriving a particular goal from a given
knowledge base and set of inference rules
• Inference rules are applied by matching the
goal of the search to the consequents of the
relations stored in the knowledge base

Definition (Ben Coppin)
In backward chaining, we start from a
conclusion, which is the hypothesis we wish to
prove, and we aim to show how that
conclusion can be reached from the rules and
facts in the database.
The conclusion we are aiming to prove is called a
goal, and so reasoning in this way is known as
goal-driven reasoning.

Backward Chaining Mechanism
(Ben Coppin)
Backward chaining starts with the goal state,
which is the set of conditions the agent wishes to
achieve in carrying out its plan. It now examines
this state and sees what actions could lead to it.
For example, if the goal state involves a block
being on a table, then one possible action would
be to place that block on the table. This action
might not be possible from the start state, and so
further actions need to be added before this
action in order to reach it from the start state. In
this way, a plan can be formulated starting from
the goal and working back toward the start state.

The benefit in this method is particularly clear in
situations where the first state allows a very large
number of possible actions. In this kind of
situation, it can be very inefficient to attempt to
formulate a plan using forward chaining because
it involves examining every possible action,
without paying any attention to which action
might be the best one to lead to the goal state.
Backward chaining ensures that each action that
is taken is one that will definitely lead to the goal,
and in many cases this will make the planning
process far more efficient.

BACKWARD CHAINING EXAMPLE
“WEATHER FORECAST SYSTEM”

• Suppose we have been given the following
facts,
a) Sun is behind the clouds.
b) Air is very heavy and cool.
• Problem: Using Backward chaining try to
conclude that there are chances of rain.

COMPARING FORWARD AND
BACKWARD CHAINING
BEN COPPIN

Let us use an example to compare forward and
backward chaining. In this case, we will revert
to our use of symbols for logical statements, in
order to clarify the explanation, but we could
equally well be using rules about elevators
or the weather.

Forward Backward Chaining

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