The document discusses forward state space planning (FSSP) and backward state space planning (BSSP). FSSP, also called progression planning, starts from the initial state and plans a sequence of actions to reach the goal state. BSSP, also called regression planning, starts from the goal state and works backward to determine which actions could have led to that state. The document provides an example of using both FSSP and BSSP to forecast weather based on given rules and facts about temperature, humidity, clouds, etc.

1. Topic To Be Covered:
I. FSSP(Progression Planner)
II. BSSP(Regression Planner)
Jagdamba Education Society's
SND College of Engineering & Research Centre
Department of Computer Engineering
SUBJECT: Artificial Intelligence & Robotics
Lecture No-06(UNIT-02)
Prof.Dhakane Vikas N

2. Forward state space planning
What is FSSP or Progression Planner In AI?
 Forward state space search or FSSP is also called as progression planner,
as we plan sequence of action starting from the initial states in order to
attain the goal state.
 With FSSP or FSSS method we start with the initial state and go to final
state.
 While doing this we need to consider the probable effects of the actions
taken at every state.
 Thus prerequisite for this type of planning is to have initial world state
information, details of available actions of the agent and description of the
goal state.
 Important point to remember here is that the available actions include
precondition & effect of that action.

3. Forward state space planning
What is FSSP or Progression Planner In AI?
 It is a strategy of an expert system to answer the question, “What can
happen next?”
 Here, the Inference Engine follows the chain of conditions and derivations
and finally deduces the outcome. It considers all the facts and rules, and
sorts them before concluding to a solution.

4. Forward state space planning
Forward State Space Planning example:-“Weather forecasting system”
 Suppose we have developed the following rules for our weather
forecasting system,
Rule I
If we suspect temperature is less than 20◦
AND there is humidity in the air
Then there are chances of rain.
Rule II
If Sun is behind the clouds
AND air is very cool.
Then we suspect temperature is less than 20◦
Rule III
If air is very heavy
Then there is humidity in the air.
Now, Suppose we have been given the following facts,
 a)Sun is behind the clouds
 b)Air is very heavy and cool

5. Forward state space planning
Forward State Space Planning example:-“Weather forecasting system”
 Now, 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”.
 As we know that forward chaining is a data driven method so we will start
from our given data(facts).
 we can brake our second fact into two facts because it is connecting by
AND.
So we have three facts)
a)Sun is behind the clouds
b) Air is very heavy.
c) Air is very cool.

6. Forward state space planning
Forward State Space Planning example:-“Weather forecasting system”
 Now, in forward chaining system we will rich goal from the given facts.
For this
 we match with the ‘IF..AND’ part of the rule base and create new fact
which one is present in the ‘Then’ part.
 In other words if we ask question “Why this happened?” to the given facts
then we will get our answer from the set of rules which are present in the
knowledge base.
a)Sun is behind the clouds and
c) Air is very cool this two facts are present in the IF..AND part of Rule II.
Thus we get our new fact “we suspect temperature is less than 20◦”
 Now we have the following facts present:
b) “Air is very heavy”
d) “we suspect temperature is less than 20◦”

7. Forward state space planning
Forward State Space Planning example:-“Weather forecasting system”
 So now, our fact (b) “Air is very heavy”
has matched with the ‘IF’ part of the Rule III.
 Thus our new fact will become
e) “there is humidity in the air”
d) “we suspect temperature is less than 20◦”
 Now, this both new fact e and d are matched with the ‘If...AND’ part of
Rule I.
 Thus our new fact will be
f)“there are chances of rain” Which is nothing but our goal state.

8. Backward state space planning
What is BSSP or Regression Planner In AI?
 Backward state space search or BSSP is also called as regression planner,
from the name of this method you can make out that the processing will
start from the finishing state and then you will go backwards to the initial
state.
 So, basically we try to backward the scenario and find out the best
possibility, in order to achieve the goal , to achieve this we have to see
what might have been correct action at previous state.
 In forward state space search we need information about the successor of
the current state now, for backward state space search we will need
information about the predecessor of the current state.

9. Backward state space planning
Backward chaining example:- We will use same example
“Weather forecasting system”
Rule I
If we suspect temperature is less than 20◦
AND there is humidity in the air
Then there are chances of rain.
Rule II
If Sun is behind the clouds
AND air is very cool.
Then we suspect temperature is less than 20◦
Rule III
If air is very heavy
Then there is humidity in the air.

10. Backward state space planning
Backward chaining example:- We will use same example
“Weather forecasting system”
 As we know that backward chaining is a goal driven method so we will
start from our goal statement and we will rich facts from the goal state.
 we can brake our second fact into two facts because it is connecting by
AND. So we have three facts
a)Sun is behind the clouds
b) Air is very heavy.
c) Air is very cool.
 So we have our goal statement “there are chances of rain”.
 if we ask question “Why this happened?” then we will get our answer in
the “Then” part of given Rules.

11. Backward state space planning
Backward chaining example:-
 So the statement “there are chances of rain” is present in the ‘Then’
part of Rule I.
 Thus we get our two sub goals
1. “we suspect temperature is less than 20◦”
2. “there is humidity in the air”
 Now sub goal number 1 is present in the ‘Then’ part of Rule II.
 Thus we get our another two sub goals
3.”Sun is behind the clouds”
4. “air is very cool”. Which is nothing but our first and third fact.(a and

12. Backward state space planning
Backward chaining example:-
 And sub goal number 2 is present in the ‘Then’ part of Rule III. Thus we
get our another sub goal
5. “Air is very heavy.” Which is nothing but our second fact(b).
Hence we conclude that “there are chances of rain” because we rich all
facts(data).