1. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 1 Lecture 8 Project Discussion Knowledge Representation Non-Formal Method Attribute-Value Pair Inference Networks Neural Networks

2. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 2 Attribute-Value Pair Aim of developing this representation is to reduce the development time especially the transition time between the rule based to the representation employed by the software package utilisation. Attributes are the antecedents/conclusions of the rule Values are the limits imposed on attributes Example: If shahid is of age 5 Then he should go to the school If X 2 Then Z=3

3. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 3 Attribute-Value Pair Example: R1if x=5 then z=2 R2if y=6 then s=1 R3if x=2 and y=2 then z=2 R4if s=1 and e=2 then d=7 RuleClauseAttributeValue R11x5 R31x2 R21y6 R32y2 R41s1 R42e2 R13z2 R33z2 R23s1 R43d7

4. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 4 Example 1: Waste Disposal R1:If waste origin = source A and waste amount = large Then waste destination = site X R2:If waste origin = source A and waste amount = modest Then waste destination = site Y R3:If waste origin = source B and waste contents = toxic Then waste destination = site Z R4:If waste origin = source B and waste contents = nontoxic Then waste destination = site Y

5. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 5 Example 1: Solution of Waste Disposal

6. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 6 Example 2: R1:If A = a and B = b Then R = r R2:If A = NOT a and C = c Then S = s R3:If S = s and B = b Then X = x

7. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 7 Example 2: Solution

8. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 8 Inference Networks Another Graphical Approach Symbols are used to present rules Box: represents assertion/antecedents Circles: represents conclusions Connectives are represented by special symbols Assertionconclusionintermediateandor conclusion

9. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 9Example: R1:If assertion 1 is true and assertion 2 is true Then conclusion 1 is established R2:If assertion 3 is true or assertion 4 is true Then conclusion 2 is established

10. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 10 Example : inference Net

11. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 11 Example 1: R1:If B and C Then G R2:If A and G Then I R3:If D and G Then J R4: If E or F then H R5: If D and H Then K

12. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 12 Example 1: inference Net

13. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 13 Neural Networks Neural Networks can be used to represent Knowledge but has a unique way of storing the knowledge Works on the principles of biological neural system Human nervous system contains more than 10 billion neurons These neurons are massively interconnected 1.Dendrites are the parts which receive impulses 2.Axons transmit these impulses 3.Between two neurons is the synaptic junction 4.If the impulse energy is high enough it will jump the synaptic junction and impulse is passed to the next neurons 5.Different Impulses activate the synaptic junction differently 6.Thus knowledge can be stored as a pool of activations that responds to its particular input impulse.

14. Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department 14 Neural Networks Artificial Neural networks have Inputsthat corresponds to dendrites Outputssynaptic jump Weightsstore synaptic activity of different inputs numeric form