Lecture 8 Project Discussion Knowledge Representation Non-Formal Method Attribute-Value Pair Inference Networks Neural Networks - PowerPoint PPT Presentation

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Lecture 8 Project Discussion Knowledge Representation Non-Formal Method Attribute-Value Pair Inference Networks Neural Networks

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Dr. M.M. Awais- Computer Science Department. 1. Lecture 8. Project Discussion ... Dr. M.M. Awais- Computer Science Department. 5. Example 1: Solution of Waste Disposal ... – PowerPoint PPT presentation

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Title: Lecture 8 Project Discussion Knowledge Representation Non-Formal Method Attribute-Value Pair Inference Networks Neural Networks


1
Lecture 8Project DiscussionKnowledge
RepresentationNon-Formal Method Attribute-Value
Pair Inference Networks Neural Networks

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 lt 10 and Ygt2 Then Z3

3
Attribute-Value Pair
  • Example R1 if x5 then z2
  • R2 if y6 then s1
  • R3 if x2 and y2 then z2
  • R4 if s1 and e2 then d7
  • Rule Clause Attribute Value
  • R1 1 x 5
  • R3 1 x 2
  • R2 1 y 6
  • R3 2 y 2
  • R4 1 s 1
  • R4 2 e 2
  • R1 3 z 2
  • R3 3 z 2
  • R2 3 s 1
  • R4 3 d 7

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
Example 1 Solution of Waste Disposal
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
Example 2 Solution
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

Assertion conclusion intermediate and or conc
lusion
9
Example
  • 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
Example inference Net
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
Example 1 inference Net
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
  • Dendrites are the parts which receive impulses
  • Axons transmit these impulses
  • Between two neurons is the synaptic junction
  • If the impulse energy is high enough it will jump
    the synaptic junction and impulse is passed to
    the next neurons
  • Different Impulses activate the synaptic junction
    differently
  • Thus knowledge can be stored as a pool of
    activations that responds to its particular input
    impulse.

14
Neural Networks
Artificial Neural networks have Inputs that
corresponds to dendrites Outputs synaptic
jump Weights store synaptic activity of different
inputs numeric form
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