developing knowledge-based systems
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Knowledge-Based Systems, Akerkar and Priti Sajja, Chapter 3TRANSCRIPT
Chapter 3Developing Knowledge-Based Systems
(Knowledge-Based Systems; R Akerkar, P Sajja)
Prepared By: Ashique Rasool
Prepared By: Ashique Rasool
Nature Of Knowledge-Based SystemsQuite different from other computer based
information systemsDeals with knowledge and works at an
unstructured levelCan justify there decision and have the ability
to learn
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Difficulties in KBS DevelopmentHigh cost and effortDealing with experts
Experts are often rare so it is difficult to meet them and take knowledge for the system
The nature of knowledgeAs 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 riskIt 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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KBS Development Model
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KBS Development ModelThis Development model is based on the
system life cycle. The major stages of this model are:Elicitation of feasible requirementsStrategy Selection and Overall Design of KBSOntology Selection and knowledge
representationSystem Development and ImplementationTesting, Implementation and TrainingKnowledge Acquisition
In the figure development round one just gives a prototype and round two gives complete system development.
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Knowledge AquisitionActivities in Knowledge Acquisition
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Knowledge Acquisition…Knowledge Eliciation
The knowledge acquisition process in which the domain expert is the only source of knowledge
Steps Of Knowledge AcquisitionStep I : Find suitable expert and knowledge
engineerStep II : Proper homework and planningStep III : Interpreting and understanding the
knowledge provided by the expertsStep IV : Representing the knowledge provided
by the experts
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Techniques for Knowledge AcquisitionLiterature reviewInterview 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)
ObservationsObserving experts in a live environment gives a better picture of the solution strategy
Diagram-Based TechniquesProcess-flow diagram, conceptual maps, event and state charts
Generating PrototypesConcept sorting
Concept SortingIt is a psychological technique that is useful in
tapping an organization's knowledge.Steps of Concept Sorting
1. Consider a textbook or ask domain expert for the basic concepts and standards of the domain and codify each major concept in separate cards
2. Arrange these cards into various groups according to their use
3. Ask question to the domain expert regarding the order and placement of the concept cards
4. Steps 2 & 3 are repeated until the expert is finished answering questions or sufficient knowledge is acquired
5. If the expert runs out of knowledge then the enginer takes any three cards and ask the relationship.
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Sharing KnowledgeExperts can share meaningful outcomes of their
learning process to enrich and generalize their knowledge. Following are the methods for knowledge sharing:
Problem SolvingTalking and story tellingSupervisory style
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Issues with Knowledge AcquisitionMost knowledge rests with experts so can
not be extracted directlyContinuously changing nature of
knowledgeDifficult to prepare the experts for
knowledge acquisition processSometimes the knowledge are
subcontiousAn expert is not always correctNo single expert know everythingOpinions among multiple experts may
differ significantlyPrepared By: Ashique Rasool
Updating knowledgeThe 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 expertsPrepared By: Ashique Rasool
Knowledge RepresentationKnowledge 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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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 knowledgeShould be able to provide reasoning and
explanationShould be able to store updates and
support incremental developmentShould be independent enough to be
reusedPrepared By: Ashique Rasool
Types Of KnowledgeKnowledge representation is broadly
classified in two categories
Factual Knowledge RepresentationConstantsVariablesFunctionsPredicatesWell-formed FormulasFirst Order Logic
Procedural Knowledge Representation
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Factual Knowledge RepresentationFactual 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 Prepared By: Ashique Rasool
Factual Knowledge RepresentationFirst Order Logic: Generated by combining
predicate logic and propositional logic.
ExamplesConstants: Mohammad, Salem etc.Variables: ManFunctions: Elder(Mohammad, Salem) returns
valuePredicates: Mortal(Salem) returns Boolean
valueWell-Formed Formulas: If you don’t
exercise you will gain weight. Represented as ∀x[{Human(x) ^ ~ ∃Exercise(x)} => Gain_Weight(x)]Prepared By: Ashique Rasool
Representing Procedural KnowledgeProcedural 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 rulesNew facts are addedCombining the new facts with existing facts and
rule
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Representing Procedural KnowledgeSemantic 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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Representing Procedural KnowledgeFrames: Frames are the description of
conceptual and default knowledge about a given entity.
A frame organizes knowledge according to cause-and-effect 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 followingExplicit or default valuesA range of valuesAn if-added type of
procedural attachment.Prepared By: Ashique Rasool
Example:Name: Power bikeBroad Category: Land vehicleSub Category:GearlessCost: $350Capacity: Two personsSpeed: 160 km/hour
Representing Procedural KnowledgeA frame based interpreter must be capable of the
following:
Check for a slot value that is correct and within specified range
Dissemination of definition valuesInheritance of default valuesComputation of the value of a slot as requiredChecking whether the correct values has been
computed
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Representing Procedural KnowledgeScripts: 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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Representing Procedural KnowledgeHybrid Structures: It encorporates more than
one representation scheme.
Example:
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KBS Tools
PROLOG
LISP (List Processing)
AIML (Artificial Intelligence Modeling
Language)
MATLAB
JavaNNS (Java Neural Networks Simulator)
CLIPS (C Language Integrated Production
System)Prepared By: Ashique Rasool