Knowledge-Based Systems, Akerkar and Priti Sajja, Chapter 3

1. Developing Knowledge-Based Systems
(Knowledge-Based Systems; R Akerkar, P Sajja)
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2. Nature Of Knowledge-Based Systems
 Quite different from other computer based
information systems
 Deals with knowledge and works at an unstructured
level
 Can justify there decision and have the ability to learn
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3. Difficulties in KBS Development
 High cost and effort
 Dealing with experts
Experts are often rare so it is difficult to meet them and take knowledge
for the system
 The nature of knowledge
As the knowledge is specific to the domain, it can not be shared
without the presence of expert even the knowledge is available
 The level of risk
It is some how risky because the development cost is very high and the
cost goes higher and higher in maintaining these systems
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4. KBS Development Model
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5. KBS Development Model
 This Development model is based on the system life
cycle. The major stages of this model are:
 Elicitation of feasible requirements
 Strategy Selection and Overall Design of KBS
 Ontology Selection and knowledge representation
 System Development and Implementation
 Testing, Implementation and Training
 Knowledge Acquisition
 In the figure development round one just gives a
prototype and round two gives complete system
development.
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6. Knowledge Aquisition
 Activities in Knowledge Acquisition
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7. Knowledge Acquisition…
 Knowledge Eliciation
The knowledge acquisition process in which the domain expert is the
only source of knowledge
 Steps Of Knowledge Acquisition
 Step I : Find suitable expert and knowledge engineer
 Step II : Proper homework and planning
 Step III : Interpreting and understanding the knowledge
provided by the experts
 Step IV : Representing the knowledge provided by the
experts
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8. Techniques for Knowledge Acquisition
 Literature review
 Interview and protocol analysis
Protocol analysis is a kind of interview in which the domain expert is
asked not only to solve the problem but also to think aloud while doing
so.
 Surveys and Questionnaires
Useful in gather quantitative factual knowledge (explicit knowledge)
 Observations
Observing experts in a live environment gives a better picture of the
solution strategy
 Diagram-Based Techniques
Process-flow diagram, conceptual maps, event and state charts
 Generating Prototypes
 Concept sorting
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9. Concept Sorting
It is a psychological technique that is useful in tapping an
organization's knowledge.
 Steps of Concept Sorting
1.
2.
3.
4.
5.
Consider a textbook or ask domain expert for the basic
concepts and standards of the domain and codify each
major concept in separate cards
Arrange these cards into various groups according to
their use
Ask question to the domain expert regarding the order
and placement of the concept cards
Steps 2 & 3 are repeated until the expert is finished
answering questions or sufficient knowledge is
acquired
If the expert runs out of knowledge then the enginer
takes any three cards and ask the relationship.
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10. Sharing Knowledge
Experts can share meaningful outcomes of their learning
process to enrich and generalize their knowledge.
Following are the methods for knowledge sharing:
 Problem Solving
 Talking and story telling
 Supervisory style
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11. Issues with Knowledge Acquisition
 Most knowledge rests with experts so can not be
extracted directly
 Continuously changing nature of knowledge
 Difficult to prepare the experts for knowledge
acquisition process
 Sometimes the knowledge are subcontious
 An expert is not always correct
 No single expert know everything
 Opinions among multiple experts may differ
significantly
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12. Updating knowledge
The knowledge base in a KBS undergoes continuous
updating. Following are the three means by which
updates can be made
 Self-Updating:
The system learns from the cases it handles(self learning)
 Manual updates by knowledge engineer
 Manual Updates by experts
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13. Knowledge Representation
Knowledge components should be represented in
such a way that the operations storage, retrieval,
inference and reasoning are facilitated without
disturbing the required characteristics of
knowledge
Knowledge Structure:
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14. Characteristics of efficient
knowledge representation facility
 It should be able to represent the given knowledge
to a sufficient depth
 Should preserve the fundamental characteristics of
knowledge(complete, accessible, consistent etc).
 Should be able to infer new knowledge
 Should be able to provide reasoning and
explanation
 Should be able to store updates and support
incremental development
 Should be independent enough to be reused
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15. Types Of Knowledge
Knowledge representation is broadly classified in
two categories
 Factual Knowledge Representation
 Constants
 Variables
 Functions
 Predicates
 Well-formed Formulas
 First Order Logic
 Procedural Knowledge Representation
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16. Factual Knowledge Representation
Factual knowledge are known as formal knowledge and can
be represented using first order logic supporting
constants, variables functions and predicates
 Constants:
Those
symbols
that
don’t
change, represent fixed knowledge
 Variables: Takes different values within a fixed
domain
 Functions: Set of instructions that carry out process
and return a predefined value
 Predicates: Special functions that return only
Boolean value
 Well-Formed Formulas: String of symbols that is
generated by a formal language
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17. Factual Knowledge Representation
 First Order Logic: Generated by combining predicate
logic and propositional logic.
Examples




Constants: Mohammad, Salem etc.
Variables: Man
Functions: Elder(Mohammad, Salem) returns value
Predicates: Mortal(Salem) returns Boolean value
 Well-Formed Formulas: If you don’t exercise you will
gain weight. Represented as
∀x[{Human(x) ^ ~ ∃Exercise(x)} => Gain_Weight(x)]
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18. Representing Procedural Knowledge
Procedural knowledge represents how to reach a solution in
a given situation. Examples of procedural knowledge are:
 Production Rules: Knowledge is represented as a
sequence of condition and the appropriate actions
If<condition>, then <action>
Rules are simple and easy to understand, implement and
modify. Large number of rules are required to solve simple
problems. This large volume creates problem in
documenting and encoding into the knowledgebase.
Deduction process works as follows:
 Knowledge in the form of facts and rules
 New facts are added
 Combining the new facts with existing facts and rule
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19. Representing Procedural Knowledge
 Semantic Networks: Graphical description of knowledge
composed of nodes (objects or concepts) and links that
show hierarchical relationships. The links carries semantic
information such as is-a, type-of, part-of etc.
Example:
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20. Representing Procedural Knowledge
 Frames: Frames are the description of conceptual and




default knowledge about a given entity.
A frame organizes knowledge according to cause-andeffect relationships
The slots of a frame contains items like
rules, facts, videos, references etc.
It also contains pointers to other frames or procedures.
A slot is further divided into facets. A facet may be any of
the following
Example:
 Explicit or default values
 A range of values
 An if-added type of
procedural attachment.
Name:
Broad Category:
Sub Category:
Cost:
Capacity:
Speed:
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Power bike
Land vehicle
Gearless
$350
Two persons
160 km/hour

21. Representing Procedural Knowledge
A frame based interpreter must be capable of the following:
 Check for a slot value that is correct and within specified




range
Dissemination of definition values
Inheritance of default values
Computation of the value of a slot as required
Checking whether the correct values has been computed
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22. Representing Procedural Knowledge
 Scripts: Script is a knowledge representation structure for
a specific situation.
 It contains slots such as objects, their roles, entry and exit
conditions and different scenes describing a process in
detail.
Example:
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23. Representing Procedural Knowledge
 Hybrid Structures: It encorporates more than one
representation scheme.
Example:
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24. KBS Tools
 PROLOG
 LISP (List Processing)
 AIML (Artificial Intelligence Modeling Language)
 MATLAB
 JavaNNS (Java Neural Networks Simulator)
 CLIPS (C Language Integrated Production System)
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